The new round of technological revolution has given rise to a new technology-economic paradigm based on artificial intelligence (AI) , which exerts comprehensive impacts on the theoretical system of management and fundamentally disrupts the underlying logic and certain theories supporting it. As a new technology-economic paradigm, AI deeply participates in management activities, profoundly reshaping the analytical framework of management theories (namely, "management environment, management subject, management object, management tools, and management mechanisms and models"). Specifically, AI-driven shifts in the "technology-economy" paradigm accelerate the transformation of management environments toward intelligent and ecological systems. AI agents emerge as key decision-making subjects in management. Management objects shift from physical spaces to hybrid digital-physical systems involving "humans, objects, data, and algorithms". Management tools evolve through human-machine collaboration toward greater intelligence. And new models and mechanisms such as super individuals, intelligent operations, and platform-based ecosystems take shape. Meanwhile, the pervasive integration of AI fundamentally challenges the core premise of "human agency" in management, generating systemic disruptions to four foundational theoretical domains: human nature assumptions, strategic decision-making, resource allocation, and value creation. Future research should accelerate the development of decision-making methods and evaluation systems for AI as a powerful management actor, explore new management models driven by AI, and construct corporate social responsibility governance frameworks in the context of artificial intelligence.

Against the backdrop of accelerating the construction of a unified national market, the transfer and reception of domestic industries hold significant practical importance for realizing the rational allocation of production factors, ensuring the integrity and security of industrial chains, and enhancing the resilience of corporate supply chains. In this context, the establishment of the national industrial relocation demonstration zones policy was treated as a "quasi-natural experiment". Based on data from companies listed on the Shanghai and Shenzhen A-share markets across 259 cities from 2008 to 2023, a multi-period difference-in-differences (DID) model was employed to analyze the effects and pathways through which industrial relocation policies enhance corporate supply chain resilience. It is found that the industrial relocation policy significantly promotes the improvement of corporate supply chain resilience and exerts sustained positive effects. These findings remain robust after a series of multidimensional robustness tests. The analysis of the underlying mechanisms reveals that the policy enhances the supply chain resilience of enterprises in receiving areas by improving their innovation capacity, promoting information sharing among supply chain firms, and maintaining supply-demand stability between upstream and downstream enterprises. Heterogeneity analysis indicates that the positive impact of the industrial relocation policy on supply chain resilience is more pronounced for private enterprises, firms in upstream segments of the industrial chain, industrial and service sector enterprises, and firms located in regions with a lower degree of market segmentation. The conclusions of this research not only offer a novel perspective for effectively enhancing corporate supply chain resilience but also provide decision-making insights for China to coordinately unleash the heterogeneous vitality of different regions, maintain the stability of the modern economic system, and promote high-quality economic development.

The supply side provides a crucial perspective for structural change research, and clarifying how factor-biased technological change affects industrial structure transformation is particularly important. Unlike developed economies with stabilized development, emerging economies are currently experiencing rapid economic expansion or transitioning towards high-quality development, characterized by high factor substitution elasticity and strong developmental flexibility. As the world's largest emerging economy, China is in a critical phase of this transition, with its factor structure and policy orientation being highly representative. Focusing on emerging economies, based on their high elasticity of factor substitution and heterogeneous rates of factor-augmenting technological progress, it develops a two-sector nested production function model. The influence mechanism of factor-biased technological change on industrial structure transformation is investigated when capital-labor substitution elasticity remains fixed, focusing on relative changes in capital and labor technological efficiency. Through theoretical derivation and numerical simulation, three major pathways are identified: the "resource allocation effect", the "capital deepening effect", and the "output scale effect". When capital technological efficiency grows faster in relatively capital-intensive sectors, capital share across industries is increased, capital intensity within industries is enhanced, and output share across industries is decreased, ultimately expanding production scale in relatively labor-intensive sectors. Moreover, when the relatively capital-intensive sector is defined as manufacturing and the relatively labor-intensive sector as services, such transformation process aligns with the direction of industrial structure optimization. Econometric tests using Chinese data further verify the above mechanisms. Implications are provided for industrial policy formulation and sustainable development in emerging economies, along with insights for technological innovation-driven modernization of industrial systems in Chinese-style modernization.

The coordinated advancement of urban digitalization and green transformation (dual-transformation coordination) represents a crucial strategy for adapting to evolving technological and environmental conditions and for cultivating new sources of competitive advantage. Drawing on panel data from 284 prefecture-level cities in China spanning 2011 to 2023, nonlinear models, curvilinear moderating effect models, and partial linear functional coefficient models were employed to empirically investigate the effects of digitalization on green sustainable innovation and the associated boundary conditions. The findings are summarized as follows. Digital transformation exerts both "driving" and "cursing" effects on green sustainable innovation. The interplay of these two underlying mechanisms generates an inverted U-shaped relationship. During the study period, the driving effect remained dominant in most Chinese cities, indicating a favorable trend in the coordinated development of digitalization and greening. "Local-neighboring" environmental regulation imposes heterogeneous nonlinear moderating effects on the relationship between local digital transformation and green sustainable innovation. Specifically, local environmental regulation intensifies both the digital "driving" and "cursing" effects, resulting in a steeper inverted U-shaped curve and a leftward shift of the inflection point. This shift causes the optimal marginal effect of digitalization on green sustainable innovation to be achieved earlier. Geographically proximate environmental regulation strengthens the inverted U-shaped relationship between local digitalization and strategic green sustainable innovation, whereas economically and geographically proximate environmental regulation strengthens the inverted U-shaped relationship between local digitalization and substantive green sustainable innovation. Estimation results derived from the partial linear functional coefficient model further reveal that "local-neighboring" environmental regulation nonlinearly moderates the dual effects of digitalization. It enriches the literature on digital transformation and green innovation, providing theoretical insights for Chinese cities as they move toward a new stage of "dual-transformation coordination" development.

The digital transformation of manufacturing is a necessary path for high-quality economic development and achieving manufacturing strength in China. The level of enterprise digital transformation is deeply influenced by the supply chain network in which it is embedded. The position within the network acts as a key lever for obtaining, integrating, and leveraging digital transformation resources. A supply chain network was constructed using data from Shanghai and Shenzhen A-share manufacturing listed enterprises and their supplier-customer relationships from 2010 to 2022. Network centrality and structural holes were used to measure an enterprise's position in the supply chain network. The impact of network position on enterprise digital transformation was analyzed and tested. It is found that higher network centrality and richer structural holes lead to a higher degree of enterprise digital transformation. Mechanism analysis shows that an advantageous network position promotes digital transformation through benefits such as commercial credit, innovation capability, and government support. Evidence is further provided on how this effect varies based on ownership nature, marketization level, and factor endowments. Additional research indicates that economic policy uncertainty weakens the promoting effect of network position advantages on digital transformation. The conclusions offer a social network perspective for advancing enterprise digital transformation. It also provides references for enterprises to acquire resources and information through supply chain networks to achieve high-quality development.

The rapid development of generative artificial intelligence (GAI) has been effective in accelerating the formation of new quality productivity and other aspects, but the governance challenges it faces in terms of data, information and content need to be solved urgently, and there is an urgent need to find a balance point between technological development and security. Based on information ecosystem theory, a collaborative governance game model involving three stakeholders (managers, providers, and users of GAI services) and numerical simulation analysis were conducted. It is found that GAI safety governance is a process of multi-object collaborative participation, which is affected by ecological factors, and increasing the co-governance gains of each subject and the losses brought by negative governance behaviors are conducive to promoting the information ecosystem to be stable in the ideal state of {strict regulation, active governance, and compliant use}. Regarding image factors, increasing the regulatory image gain of managers and the reputation image gain of providers can promote the information ecosystem to evolve to the ideal state. Regarding rewards and penalties, increasing the rewards from managers to providers and from providers to users can promote the evolution of the entire information ecosystem to a desirable state, even though it may cause a slight loss in the interests of the party who imposes the rewards, where the rewards given by the managers are within an effective threshold; and increasing the penalties imposed by the managers and providers can promote the synergistic governance of the three parties. With regard to the factor of excess earnings, compressing the space for excess earnings of providers and users can promote the synergistic governance of the three parties. The research conclusions presented herein provide theoretical support for formulating security governance strategies for generative artificial intelligence technologies, thereby promoting the development of a healthy and orderly online ecosystem.

While empowering economic and social development, artificial intelligence (AI) is also reshaping the internal pay distribution structure within firms. Taking the establishment of the National New-Generation Artificial Intelligence Innovation and Development Pilot Zones as a quasi-natural experiment, based on a sample of Chinese A-share listed companies from 2015 to 2023, the impact of the pilot zones on intrafirm pay gaps and its underlying mechanisms was invetigated. The findings show that the establishment of the AI pilot zones significantly narrows intra-firm pay gaps, and this result remains robust after a series of robustness tests such as replacing the explained variable. Mechanism analysis indicates that the narrowing effect of the AI pilot zones on intra-firm pay gaps is likely achieved by lowering managerial risk compensation, curbing earnings management, and enhancing ordinary employees' bargaining power. Heterogeneity analysis reveals that, from the perspective of internal characteristics, the impact of the AI pilot zones on intra-firm pay gaps is more pronounced in small and medium-sized enterprises, nonstate-owned enterprises, and labor-intensive firms. From the perspective of external environment, this effect is more evident among firms located in eastern and western China, as well as those subject to weaker external audit oversight. Further analysis demonstrates that the reduction in intra-firm pay gaps is mainly reflected in the narrowing of excessive pay disparities. The conclusions provide empirical evidence for revealing the internal mechanism through which artificial intelligence affects intra-firm pay gaps, and also offer important references for achieving the goal of common prosperity in the digital economy era.

As a pivotal institutional tool for digital government construction and the market-oriented reform of production factors, the economic impact of public data openness on micro-level corporate behavior was systematically evaluated. Based on a sample of Chinese A-share listed companies from 2009 to 2023, the causal effect of public data openness on corporate performance and its underlying mechanisms were identified by employing a multi-period difference-in-differences (DID) model integrated with city-level pilot information. A significant positive effect of public data openness on corporate performance was revealed through benchmark regression. The reliability of this conclusion was confirmed by a series of robustness tests, including parallel trend tests, placebo tests, PSM-DID estimation, and the exclusion of contemporaneous policy interferences such as the "Broadband China" strategy and National Big Data Comprehensive Pilot Zones. Mechanism analysis indicates that corporate performance is enhanced through two primary transmission pathways: the alleviation of financing constraints and the promotion of corporate innovation. By serving as an authoritative signaling mechanism, public data openness reduces information asymmetry in credit markets and lowers credit acquisition costs. Simultaneously, it provides low-cost knowledge reserves that reduce R&D trial-and-error costs and stimulate patent output. Heterogeneity analysis further demonstrates that the performance-enhancing effects are more pronounced among enterprises in eastern regions, small-scale enterprises, non-state-owned enterprises, and non-high-tech firms. These findings provide empirical evidence and policy orientation for optimizing public data supply quality, mitigating the digital divide, and fostering the efficient utilization of data elements.

Innovation policies, especially government R&D subsidies as a direct innovation policy instrument, play an important guiding role in technological innovation and contribute to the development of new quality productive forces. Government effectiveness is recognized as a key contingency factor influencing the implementation efficiency of such policies. A comprehensive index of new quality productive forces was constructed, and provincial panel data were employed to empirically examine the impact of government R&D subsidies on new quality productive forces. Following a rigorous theoretical framework, the moderating mechanism and threshold effect of government effectiveness were further tested. The results show that government R&D subsidies significantly promote the development of new quality productive forces, and this conclusion remains robust across multiple robustness checks. The subsidy effect exhibits a smooth nonlinear characteristic, where the marginal promotion effect strengthens with increasing subsidy intensity. Government effectiveness exerts a positive moderating effect on the relationship between R&D subsidies and new quality productive forces. Results from the panel threshold model indicate that government effectiveness has a threshold characteristic, and in the interval of higher government effectiveness, R&D subsidies have a stronger facilitating effect on new quality productive forces. Heterogeneity analysis reveals that the policy effect is statistically significant and more pronounced in central and western China, whereas it is not significant in eastern regions under the current sample and model specification. Based on these findings, policy implications are proposed, including improving the design of the innovation policy system, enhancing government effectiveness, and implementing regionally differentiated strategies. It provides empirical evidence for understanding the governance contingency of innovation policy performance and offers insights for optimizing the institutional environment for the development of new quality productive forces.

The green transformation of industrial firms is widely recognized as crucial. Using the establishment of environmental courts as a quasi-natural experiment, an investigation was conducted into how strengthened environmental rule of law addresses green transformation challenges. Existing measurements of corporate green transition face notable limitations: proxy variables and comprehensive indices offer limited coverage, while text analysis methods relying on predefined dictionaries struggle with complex semantics. To address these issues, an innovative approach utilizing large language models to analyze annual reports was adopted to construct a corporate green transition indicator. Based on data from Chinese listed industrial enterprises (from 2004 to 2023), the establishment of environmental courts was employed to examine how strengthened environmental rule of law resolves green transition challenges. Through a staggered DID methodology, it is revealed that environmental court establishment significantly promotes corporate green transition, with more pronounced effects observed in state-owned enterprises, large firms, and those located outside the Yangtze River Economic Belt or in low-rule-of-law regions. Mechanism analysis shows this transition is driven through external oversight and internal governance channels. It is further found that environmental court establishment contributes to reduced pollution emissions, and that corporate green transition subsequently mitigates idiosyncratic risks. Theoretical support is thus provided for strengthening the environmental rule of law, and new pathways are offered for achieving industrial green transformation.

The development of new quality productive forces is regarded as a crucial measure for fully building a great modern socialist country, and the construction of the social credit system is a key link in cultivating enterprises' new quality productive forces. A sample of A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2011 to 2023 was selected, and the double machine learning model was adopted to explore the impact and transmission channels of the construction of the social credit system on enterprises' new quality productive forces. Results show that the construction of the social credit system significantly promotes the improvement of enterprises' new quality productive forces. The above promoting effect remains significant after a series of robustness tests, including the replacement of sample periods, the replacement of explained variables and the exclusion of parallel policies. The heterogeneity analysis reveals that the promoting effect of the social credit system construction on enterprises' new quality productive forces is heterogeneous based on enterprises' own characteristics. The promotion is more prominent in non-state-owned enterprises, high-tech enterprises, enterprises with low capital density, poor governance capacity and weak external supervision. The results of the mechanism analysis indicate that the construction of the social credit system effectively drives the development of enterprises' new quality productive forces by alleviating corporate financing constraints, improving trade credit financing and enhancing the quality of information disclosure. The economic effects of the social credit system construction are further interpreted, and the practical path for the in-depth integration of the social credit system construction and the cultivation of new quality productive forces is revealed, which provides beneficial references for the cultivation of new quality productive forces in China.