The deep integration of artificial intelligence (AI) into scientific research is driving a systemic transformation in the mode of scientific knowledge production. Understanding this mechanism is essential for advancing technological innovation and developing new quality productive forces in China. This study conducts a case study of the Robotic AI-Chemist platform at the University of Science and Technology of China. Drawing on case study and theoretical analysis, the study makes three contributions. First, it proposes a four-dimensional synergistic mechanism through which AI transforms the mechanism of scientific knowledge production. Once embedded in the practice of scientific knowledge production, AI holistically reconfigures researchers' cognitive patterns, technology-tool forms, organizational structures, and institutional systems, rather than merely adding to a single technological dimension. Second, the study finds that the four units of knowledge production—cognition, technology, organization, and institution—exhibit new features in both content and structure. At the cognitive level, AI shifts scientific reasoning from experience-oriented to algorithm-driven, and expands researchers' solvable problem space and cognitive boundaries. At the technological level, AI drives the evolution of research tools from discrete execution toward integrated intelligent platforms. At the organizational level, human-machine collaborative innovation moves collaboration networks from disciplinary closure toward interdisciplinary open collaboration. At the institutional level, rules for evaluation and incentives, resource allocation, and data governance dynamically adapt to the demands of intelligent research. Third, the interactions among cognition, technology, organization, and institution take on new content and forms. These interactions do not emerge naturally. Instead, they are continuously adjusted in response to a series of tensions and conflicts brought by AI, ultimately advancing scientific knowledge production toward a human-machine-knowledge synergistic system. This study extends the applicability and explanatory power of theories on the mode of scientific knowledge production and its system structure in the age of intelligence. It provides important support for accelerating the AI-led transformation of research paradigms.

Against the strategic backdrop of the "AI+" initiative and the promotion of in-depth integration of the innovation chain, industrial chain, capital chain, and talent chain, this study systematically examines the impact mechanism of artificial intelligence on enterprises' market competitiveness from the perspective of the "four chains" using data from Chinese A-share listed companies from 2007 to 2024. The empirical results show that artificial intelligence (AI) exerts a significant enabling effect on enterprises' market competitiveness, and this conclusion remains valid after a series of robustness tests and endogeneity treatments. The enabling effect presents significant heterogeneity:it is more pronounced in small and medium-sized enterprises with low management ownership, as well as firms operating in highly competitive industries, central cities, and regions with a high degree of marketization. Mechanism tests further demonstrate that AI promotes the improvement of corporate competitiveness mainly through four paths:improving the health of the capital chain, optimizing the structure of the talent chain, enhancing the capability of the innovation chain, and boosting the resilience of the supply chain. Moreover, there exists a significant synergistic effect among the "four chains":the capital chain provides the resource foundation, the talent chain delivers the core competence, the innovation chain creates value increments, and the supply chain realizes the transformation of achievements, collectively forming an organic system for AI to empower corporate competitiveness. This study offers a new perspective for understanding the micro-mechanism of AI-driven corporate competitiveness enhancement and provides valuable policy implications for advancing the intelligent transformation of enterprises and the in-depth integration of the "four chains".

Promoting the deep integration of artificial intelligence (AI) and manufacturing is critical for driving high-quality development in the manufacturing sector and enhancing capacity utilization. Using a sample of A-share manufacturing firms listed on the Shanghai and Shenzhen stock exchanges between 2008 and 2022, this study applies Python text analysis to construct AI metrics and employs a panel fixed-effects model to investigate how the integrated innovation of AI with manufacturing technology impacts capacity utilization and its heterogeneous effects across technology types. The findings reveal that AI technology innovation and manufacturing technology innovation both significantly improve capacity utilization, with their integration yielding an even greater enhancement. Compared to exploitative manufacturing technologies, the integrated innovation of AI with exploratory manufacturing technologies produces a more pronounced capacity-enhancing effect. The integrated innovation of AI with manufacturing technology positively affects capacity utilization by improving asset flexibility and product competitiveness. This study contributes to the understanding of AI's enabling characteristics and offers valuable insights for improving capacity utilization.

Over-reliance on AI and technological alienation may induce "AI counter-productivity", which reversely constrains the transformation of scientific and technological achievements, yet its underlying mechanism remains unclear. From a dual-system perspective, this study constructs a "techno-social dual nudge" framework and employs scenario experiments, questionnaires, and a longitudinal case study to examine the impact of AI counter-productivity on transformation and its mechanisms. Findings reveal that AI counter-productivity negatively affects team knowledge decisiveness; the transactive memory system (TMS) mediates this relationship, and AI counter-productivity sequentially corrodes TMS along its three dimensions of specialization, credibility, and coordination; team psychological safety (TPS) positively moderates the negative impact of AI counter-productivity on TMS, with the indirect effect being weakened under high TPS, and activates the immune mechanism of the social boosting system through ritualized dialogue, inclusive trust, and clarified authorization. This study reveals the team cognitive transmission path of AI counter-productivity, extends the theory on negative effects of technology, and provides practical guidance for constructing techno-social dual-system synergy in transformation risk management.

Breaking through the "bottleneck" of key core technologies is a central task in achieving technological self-reliance and national strength. Driven by national innovation development strategies, Chinese government agencies have, in recent years, intensively introduced a series of science and technology policies, aiming to accelerate the campaign for breakthroughs in critical technologies through a coordinated "policy bundle". This study selects listed enterprise groups among China's top 500 manufacturing firms as the research sample and conducts a quantitative analysis of national-level science and technology policy documents issued between 2012 and 2020. It systematically investigates the effects of policy mix consistency and equilibrium on firms' capacity to achieve breakthroughs in key core technologies. The empirical results reveal that both consistency and equilibrium of policy mixes exhibit an inverted U-shaped relationship with technological breakthroughs, indicating the existence of an optimal policy configuration range. However, no significant synergistic effect between the two dimensions was observed. This study not only provides new empirical evidence and theoretical interpretation for ongoing academic debates on the effectiveness of science and technology policy innovation, but also offers decision-making insights—based on empirical analysis—for optimizing China's science and technology policy system and designing tailored policy portfolios aligned with the developmental needs of key core technologies.

Based on data from Chinese manufacturing listed companies from 2010 to 2023, this study takes the establishment of the National Big Data Comprehensive Pilot Zone as a quasi-natural experiment and employs a multi-period difference-in-differences model to empirically analyze the effects of big data policies on the total factor productivity (TFP) of manufacturing firms, including their impact mechanisms and heterogeneity across different types of enterprises. The findings indicate that big data policies significantly and effectively promote the improvement of TFP in manufacturing firms. Mechanism tests reveal that big data policies enhance TFP by strengthening internal control and management efficiency, intensifying market competition, and improving supply chain resilience. Heterogeneity analysis shows that the promoting effect of big data policies on TFP is more pronounced in state-owned enterprises and non-regulated industries. Additionally, the policies significantly boost TFP in enterprises located in the eastern and central regions, while their impact on firms in the western region is not significant. The research conclusions provide decision-making support for the government in promoting corporate digital transformation and facilitating high-quality economic development.

In the wave of digital economy, the coupling and coordination of data and technology elements have become a key driver of enterprises' disruptive innovation. From the perspective of knowledge foundations, using patent knowledge width and talent knowledge transfer degree as mediating variables, and intellectual property protection as a moderating variable, this study combined data from China's energy storage listed enterprises (2012-2021) and employed a panel negative binomial model to explore the nonlinear relationship between the coupling coordination of regional data and technology elements and disruptive innovation. The findings revealed that:Firstly, the non-rivalrous and increasing returns to scale characteristics of data elements endow their coupling coordination with technology elements with dual attributes of resource allocation and incentive constraints, leading to an inverted U-shaped impact on disruptive innovation. Secondly, enterprises transform external innovation resources derived from element coordination into internal resources through knowledge management capabilities, including patent knowledge width and talent knowledge transfer degree, which subsequently influence disruptive innovation. This finding validated the "element coordination-knowledge management-innovation disruption" transmission mechanism. Thirdly, an increased level of intellectual property protection may trigger conflicts over property rights allocation and reinforce path dependence, thereby progressively weakening the relationship between element coordination and disruptive innovation. The findings will provide theoretical support for enterprises to implement data-driven disruptive innovation practices, and have implications for science and technology policy making.

Insufficient innovation ecosystem and low innovation efficiency and quality are the main problems constraining the development of regional innovation in China, and the development of digital economy provides a new direction for solving these problems. Based on the panel data of provincical-level regions from 2013 to 2022, this paper estimates regional innovation performance from the perspective of innovation ecosystem, and explores the impact of digital economy on regional innovation performance by constructing a dual machine learning model with the "National Pilot Zone for Innovative Development of Digital Economy" as a quasi-natural experiment. The results show that:(1) the development of digital economy can significantly improve the regional innovation performance, which is mainly reflected in the performance improvement of the government support subsystem within the innovation ecosystem. (2) The degree of synergy between subsystems within the innovation ecosystem plays a significant mediating role in the enhancement of regional innovation performance driven by the digital economy, while neither the symbiosis level of the innovation ecosystem nor the innovation efficiency of subsystems shows such mediation. (3) There is regional heterogeneity in the enhancement of innovation performance by the digital economy, with more significant effects in the Beijing-Tianjin-Hebei region, economically developed regions, and digitally advanced regions. This paper not only broadens the theoretical perspectives on the digital economy's impact on regional innovation but also offers practical insights for leveraging digital economy development to stabilize the innovation ecosystem.

Empowering regional innovation through the deep integration of digital and real economy is a crucial pathway for accelerating the construction of a new development pattern and driving high-quality economic development. This holds essential significance for the implementation of China's innovation-driven development strategy. Based on the innovation data spanning from 2013 to 2022 across 30 provincial-level regions in China, this paper uses the panel entropy and SBM-DEA methods to compute indices for digital economy development level and regional innovation efficiency, and analyzes the influence of the digital economy on regional innovation performance from three aspects:regional innovation inputs, outputs, and efficiency utilizing the panel Tobit model. The findings suggest that (1) the digital economy exhibits a positive correlation with both regional innovation inputs and outputs. However, it significantly impedes regional R&D efficiency and demonstrates no obvious impact on regional transformation efficiency, indicating an efficiency paradox under the dual growth in both inputs and outputs. (2) The heterogeneity analysis uncovers that three factors, i.e. economic development level, intellectual property protection, and data elements scale, not only positively moderate the stimulatory impact of the digital economy on regional innovation inputs and outputs but also correct the adverse influence of the digital economy on R&D efficiency, thereby mitigating the problem of low innovation efficiency to a certain extent. (3) On this basis, in view of resource misallocation, the underlying causes of the paradox are further investigated. It is revealed that the efficiency paradox in R&D progress stems from the redundancy of R&D personnel and the inadequate output of effective invention patents, whereas transformation efficiency paradox is solely associated with input redundancy. The conclusions reveal the dual characteristics of "creativity" and "destructiveness" of the digital economy, and provide a theoretical support and practical reference on China's digital economy development and regional innovation performance improvement.

The Low-Carbon City and Innovative City Pilot Programs are concrete measures to implement the new development philosophy and innovation-driven strategy, playing a crucial role in advancing China's green transition and high-quality development. Using panel data from 287 prefecture-level cities in China, this study employs a staggered difference-in-differences (DID) model to examine the impact and mechanisms of these dual pilot policies on urban green innovation. The findings reveal:(1) The dual pilot policies exhibit a synergistic effect of "1+1>2" in stimulating urban green innovation vitality; (2) The dual pilot policy enhances urban green innovation levels through increased fiscal support, improved resource allocation efficiency, and promotion of corporate innovation investment, demonstrating a three-tier transmission mechanism of "fiscal boost-resource optimization-innovation incentive"; (3) The green innovation effects of the dual pilot policy exhibit significant differences across industries, scales, and locations, with these differences stemming from systemic variations in factor concentration, market maturity, and policy response capacity. By clarifying the mechanisms through which Low-carbon and Innovative City policies drive green innovation, this study enriches the literature on the synergy between environmental and innovation policies in the context of China's high-quality development. It provides theoretical insights and policy recommendations for optimizing the implementation of dual pilot programs and fostering urban green innovation development in China.

The unique uncertainties embodied in China's climate policies are guiding a profound transformation in firms' green innovation. Drawing on real options theory and incorporating a temporal heterogeneity perspective together with organizational behavior analysis, this study systematically constructs an analytical framework for how city-level climate policy uncertainty (CCPU) influences green managerial innovation (GMI) and the persistence of green technological innovation (GTIS). Using data from Chinese listed firms from 2010 to 2023, we employ a random forest regressor and a fixed-effects model to jointly test the nonlinear impact of CCPU on GMI and GTIS. The main findings are as follows:(1) Feature importance analysis and the Boruta algorithm indicate that CCPU is a key factor affecting firms' GMI and GTIS. (2) Partial dependence plots reveal that the effect of CCPU on GMI follows an inverted U-shape, whereas its effect on GTIS exhibits a U-shaped pattern. (3) Further analysis shows that greater strategic aggressiveness amplifies the impact of CCPU on both GMI and GTIS; a larger pay gap within the firm weakens this impact; and for firms designated as "specialized, refined, distinctive and innovative" (SRDI), the marginal effect of CCPU on GMI and GTIS becomes more pronounced. This study builds a dialogue bridge between green innovation and decision-making theories from a behavioral perspective, extends the analysis of external drivers of green innovation, and will provide important insights for national climate policymaking and corporate green innovation.

In the digital economy era, accelerating corporate digital innovation is a crucial force for high-quality economic development. Based on dynamic capabilities theory and the characteristics of digital mergers and acquisitions (M&As), this paper presented a theoretical analytical framework to examine the effects of cross-border digital M&As on corporate digital innovation. Empirically, we employed a multi-period difference-in-differences (DID) method with detailed panel data from Chinese A-share listed multinational companies from 2010 to 2022. We found that:(1) Cross-border digital M&As exert a significant, lasting, and robust positive promoting effect on corporations' digital innovation. (2) This effect is achieved through three channels:enhancing corporate executives' digital literacy, optimizing the human capital structure, and accelerating corporate digital transformation, thereby effectively promoting corporations' dynamic capabilities for digital innovation. (3) Heterogeneity analyses show that the positive impacts are particularly pronounced in digital industry companies, state-owned enterprises, and large firms. This paper enriched the application of dynamic capabilities theory in digital scenarios by examining cross-border digital resource integration. It confirms that cross-border digital M&A is an important pathway for corporations to acquire international digital resources and enhance their dynamic capabilities for digital innovation, which will provide valuable insights for formulating digital innovation strategies and optimizing cross-border investment policies.

With the deep penetration of digital technologies, while pursuing efficiency and competitiveness, enterprises are also confronted with digital ethical risks such as data misuse and algorithmic discrimination. Consequently, corporate digital responsibility (CDR) has increasingly attracted scholarly attention. However, extant studies on CDR still face challenges such as ambiguous conceptual definitions, fragmented dimensional structures, and weak measurement approaches, which constrain further theoretical exploration. This study aims to construct a structural framework and measurement scheme for CDR. First, building on a review of extant conceptual research, this study develops a digitalization-centered structural framework of CDR. In this framework, corporate social responsibility serves as an overarching value anchor at the upper level, while digital elements, digital technologies, digital management, and digital value constitute the core architecture following a dynamic "starting point-process-governance-outcomes" logic, through which CDR is articulated and operationalized. Second, a measurement scheme for CDR is designed by integrating procedures and methods including the construction of a feature lexicon, natural language processing, corporate impression management correction, and data calibration. Finally, the automotive manufacturing industry is selected for empirical measurement, providing initial evidence for the effectiveness of the proposed scheme. This study contributes to enhancing the clarity and measurability of CDR concept, not only laying a foundational for variable construction and measurement in quantitative research, but also providing referential indicators and a feasible scheme for assessing CDR.

Strengthening the standardization of digital technology is an important means to eliminate the "Digital Island" in the supply chain. This article selects the supply chain data of China's listed companies from 2010 to 2022 to screen and identify the digital technology standards developed and released by the companies, and examine the impact of customer companies' digital technology standardization on supplier company innovation. The study found that the standardization of digital technology in customer companies significantly promoted the innovation of supplier companies. Mechanism analysis indicates that the standardization of digital technology in customer companies can promote the innovation of supplier enterprises by bridging the "Standardization Gap" mechanism and the resource empowerment mechanism of the supply chain. Heterogeneity analysis reveals that when the market independently sets digital technology standards for categories, and in scenarios where supply chain with higher information transparency and the innovation level and standardization level of customer companies surpass those of supplier companies, the innovation-enabling effect of customer companies' digital technology standardization on supplier companies is more significant. This paper, based on the perspective of supplier innovation, provides micro-level evidence of the innovation-enabling effects of digital technology standardization at the supply chain level, and will offer a new perspective for high-quality advancement of enterprise digital transformation and high-quality development of supply chain enterprises.

Under the context that the state has explicitly proposed to systematically enhance the capital market's support for the real economy, it is crucial to more meticulously analyze whether venture capital, as a form of direct financing, promotes corporate innovation through the capital selection effect (screening for high-innovation enterprises) or the capital governance effect (optimizing innovation strategies). Compared with traditional studies that simply use PSM and other methods to overcome endogeneity problems, this study takes 1946 companies listed on the A-shares from 2010 to 2019 as the initial sample. Based on ordinary regression, it innovatively constructs a two-sided matching model to parse the selection mechanism through which VC institutions and enterprises reach investment deals, and more intuitively and effectively eliminates the interference of selection effect on the conclusion. The results show that:first, venture capital does promote corporate R&D investment as an innovation strategy, but there is a lag, and it is subject to interference from the selection effect; second, the strategic fit between VCs and high R&D investment enterprises can better promote corporate innovation, without lag and with no interference from the selection effect; third, the selection effect mainly produces negative interference, and its support for corporate innovation is closer to "helping in times of need" rather than "adding icing on the cake". Therefore, this study refines the theoretical understanding of how VC promotes corporate innovation after considering endogeneity, and warns VC institutions to start paying attention to this issue from the source of selection.

Government procurement of key core technologies, as an important demand-side policy for supporting "little giant" enterprises, is a significant path to enhancing the key core technological innovation capabilities of these enterprises. Based on the "Directory of Industrial Foundation Innovation and Development (2021 Edition)", this paper used text analysis to identify the content of government procurement of key core technologies. Using the data of "little giant" listed companies from 2015 to 2023 as samples, a two-way fixed effect model was employed to study the impact and mechanism of government procurement of key core technologies on the key core technological innovation capabilities of "little giant" enterprises. The research results showed that:(1) Government procurement of key core technologies significantly enhances the key core technological innovation capabilities of the "little giant" enterprises that are procured; (2) Government procurement of key core technologies comprehensively enhances the key core technological innovation capabilities of "little giant" enterprises through four aspects:increasing expected market returns, strengthening executives' core technology cognition, promoting organizational cross-border integration, and alleviating financing constraints; (3) The analysis of policy synergy effects indicates that the combination of bilateral policies is more effective in significantly enhancing the key core technological innovation capabilities of "little giant" enterprises than a single policy, and the implementation strategy of "subsidy first, then procurement" has the best effect, while other strategies have no significant impact; (4) The heterogeneity of the innovation effect of government procurement of key core technologies is tested based on the type of procurement and enterprise characteristics. (5) Further research reveals that a good supply and demand interaction relationship between government departments and "little giant" enterprises promotes the improvement of the key core technological innovation capabilities of these enterprises. This paper enriches the micro-level research on government procurement, and will provide practical evidence for optimizing the key core technological innovation policies for "little giant" enterprises.

Promoting the innovative development of SMEs in strategic emerging industries is a key measure to occupy the commanding heights of the new round of technological and industrial revolutions.This article takes SMEs in strategic emerging industries listed on the Shanghai and Shenzhen A-shares from 2010 to 2023 as the research sample, constructs a digital embedding index from four dimensions—digital technology, data elements, digital networks, and digital responsibility, and systematically examines the impact of digital embedding on the innovative development of these SMEs.The study finds that digital embedding can significantly promote the innovative development of SMEs in strategic emerging industries.Mechanism tests show that digital embedding mainly empowers the innovative development of SMEs in strategic emerging industries through three pathways:stimulating innovation motivation, creating market opportunities, and enhancing transformation capabilities.Heterogeneity analysis indicates that the promoting effect of digital embedding on the innovative development of SMEs in strategic emerging industries is more significant when the government's digital policy supply is more complete, senior executives have higher digital literacy, and the level of industry competition is lower.This study provides theoretical support and empirical evidence for achieving high-quality development of SMEs in strategic emerging industries under the context of digital integration, and also provides policy references for the government in promoting the construction of a modern industrial system.

Promoting the integration and development of innovation chain, industrial chain and supply chain is the key task of realizing China's scientific and technological self-reliance and building a modern industrial system. This paper takes the identification of chain owners as a quasi-natural experiment, takes the data of A-share listed companies from 2014-2024 as a sample, constructs a multi time point dual difference model, and tests the impact of the identification of chain owners on collaborative innovation among enterprises in the chain. The study found that the identification of chain owner enterprises can promote collaborative innovation among chain enterprises, which is manifested in the establishment of collaborative innovation at the substantive, strategic, innovation quality and effectiveness levels. Chain owner enterprises are the key mechanism to promote collaborative innovation among chain enterprises by giving full play to the head goose effect and resource effect. The analysis of heterogeneity shows that this promotion effect is more prominent in the main chain enterprises with state-owned background, large-scale and international experience, as well as in the scenarios of high dependence on industrial chain supply chain, high external environmental risk and low participation in global value chain. Further research shows that the identification of chain owners can not only reduce the external technology dependence of the industrial chain and supply chain, but also enhance their participation in the global value chain. This paper expands the theoretical research on collaborative innovation between chain owner enterprises and industrial chain supply chain, finds the positive externalities of chain owner enterprises, provides empirical evidence for understanding the role and effect of chain owner enterprises, and also confirms the positive significance of chain owner enterprises in building a modern industrial system.

Managerial ability is a critical antecedent to achieving corporate sustainable development. Examining how managerial ability influences corporate sustainable development performance through the lens of decision bias correction is of great significance for understanding how firms can optimize governance structures, enhance green competitiveness, and create long-term value. Drawing on behavioral decision theory, this study takes A-share listed companies in Shanghai and Shenzhen from 2010 to 2021 as the research sample, and conducts empirical tests using a two-stage DEA-Tobit regression model along with multiple regression methods. This study reveals that managerial ability significantly contributes to corporate sustainable development performance. Specifically, managerial ability prompts enterprises to formulate and implement sustainable development decisions by correcting myopia bias, policy cognitive bias and information asymmetry bias. Executive compensation demonstrates an inverted U-shaped moderating effect on the relationship between managerial ability and sustainable development performance. Low-intensity incentives tend to induce managerial myopia and damage the long-term sustainable development of enterprises, while high-intensity incentives trigger short-term benefit locking bias and thereby weaken the effectiveness of managerial ability. Media attention plays a positive moderating role. It constrains and optimizes the decision-making environment by reducing managerial information asymmetry and raising the cost of myopic decisions, to enhance the effect of managerial ability on sustainable development performance. This study contributes to the literature by expanding the application of managerial ability research and the behavioral decision theory in the sustainability context. It also provides practical insights for optimizing executive incentive systems and guiding media supervision more effectively.

Promoting high-quality development of manufacturing and advancing industrial technological upgrading constitute a crucial strategic pathway to achieving Chinese modernization.The introduction and application of intelligent equipment play a pivotal role in accelerating technological innovation and industrial transformation.From the perspective of the transition from embodied technology to empowering technology, this study constructs an indicator of intelligent equipment import capital stock based on customs import data and patent data of China's listed manufacturing firms, and employs a fixed-effects model to systematically examine its impact on enterprise innovation and the underlying mechanisms.The results show that:(1) Compared to ordinary devices, the innovation effect of imported intelligent equipment is more prominent, revealing the inherent logic of the transformation of technological forms from embodiment oriented to empowerment oriented.The human-machine enhancement effect and the improvement of supply chain efficiency are the key paths for its effectiveness, and external digital conditions show a stronger positive regulatory effect; (2) The innovation effect exhibits heterogeneity across firms, with state-owned, private, non-subsidized, and capital intensive or technology intensive enterprises benefiting more prominently; (3) Further analysis reveals that intelligent equipment imports significantly promote multiple types of innovation activities, including digital and non-digital innovation as well as process and product innovation, demonstrating the cross-category and universal driving effects of empowering technology.These findings enrich the theoretical framework of how empowering technology influences enterprise innovation and provide important implications for policy-making and the practice of intelligent transformation in enterprises.