The significance, challenges and opportunities of driving innovative development in manufacturing industry with artificial intelligence technologies including large language models were explored. The technological system of large language models was analyzed, its basic engineering concepts were clarified, and the pan-${\mathrm{L}}_{\mathrm{C}}$ theory-a scientific explanation for next token prediction-was presented. Based on the theory, the causes and consequences of some weird behaviors of large language models were explained, giving a more comprehensive and in-depth understanding of large language models. On the basis, three main requirements for artificial intelligence technologies in manufacturing industry were sorted out, and the core difficulties in the integration of large language models and the artificial intelligence brute-force technology were revealed. A closedness-based solution is proposed for the construction of artificial intelligence systems in manufacturing sectors, such that these systems satisfy the main requirements of specialization, logical validity and knowledge ability, as well as explainability and controllability. Finally, the trend of shifting from "industrial application of new technologies" to "sector innovation driven by new technologies" in the high-quality development of manufacturing industry is discussed briefly.

The development of AI large models is reshaping the innovation model driven by technology-push and demand-pull, making the interaction mechanisms between the two more closely integrated. However, existing literature lacks a systematic discussion on the innovation process driven by the interaction between demand and technology under the influence of AI large models. For this reason, a case study of AI large model-empowered innovation in the Tmall Genie product was conducted, based on the perspectives of the technology track and market track. The pathways for technology-push, demand-pull, and dual-track interactive innovation enabled by AI large models were extracted. The findings indicate that traditional AI technologies contribute to technology-push innovation by participating in stages such as technology identification, market validation, and testing, while also embedding in demand-pull innovation through stages like user need acquisition, evaluation, and transformation, facilitating the discovery and realization of personalized demands. AI large models enable the synergistic evolution of technology and demand, and support industry upgrading by promoting innovation ideation, technological advancement, bidirectional interaction, iterative innovation, knowledge expansion, and transformation. Compared with the innovation diffusion under the weak coupling mode between technology and demand driven by traditional AI, AI large models, with their significant advantages in expanding "user attributes" "innovator roles" and "knowledge domains" promote innovation diffusion under the strong coupling mode between technology and demand. It provides theoretical foundations and practical insights for enterprise innovation management and industrial upgrading empowered by AI large models.

The Third Plenum of the 20th Central Committee of the Communist Party of China further emphasized the need to accelerate the cultivation of new quality productive forces, and pointed out the construction of a national integrated technology and data market, and the promotion of market-oriented reforms in factors. New quality productive forces is the inheritance and innovation of Marxist productivity theory, and data elements interact with other production factors through their own characteristics, thus nurturing new quality labor materials, nourishing new quality labor objects, shaping new quality labor force, and becoming an important quality of new quality productive forces. But this process is also accompanied by practical problems such as insufficient labor capacity, imbalanced supply and demand of labor objects, unsmooth transformation of labor materials, imperfect technological innovation mechanisms, and insufficient development of emerging industries, which hinder the high-quality development of new quality productive forces. In view of this, it is urgent to take important discourse on new quality productive forces as guidance, build a comprehensive talent training system, and consolidate the source of the formation of new quality productive forces. Promote market-oriented allocation reform of data elements and smooth the path of new quality productive forces growth. Form a tripartite collaboration among the government, market, and enterprises to improve the support system for new quality productive forces. Deepen the construction of the scientific and technological innovation system to adapt to the rapid changes in new quality productive forces. Build strategic emerging industrial clusters, build a solid foundation for the development of new quality productive forces, and then accelerate the formation of new quality productive forces to promote Chinese path to modernization.

Based on the data of A-share listed companies in Shanghai and Shenzhen from 2012 to 2022 to measure the level of new quality productive forces of enterprises (NQP), a multi-period difference-in-differences model was constructed to study the impact of data factor agglomeration on the new quality productive forces of enterprises with the national-level big data comprehensive experimental zone as a quasi-natural experiment. It shows that data factor agglomeration promotes the development of new quality productive forces of enterprises, and this conclusion still holds after PSM-DID, placebo test and other robustness tests. Mechanism tests show that data factor agglomeration can empower the development of firms' new quality productive forces by improving human capital level and promoting green technology innovation; with the increase of industry competition and media attention, the role of data factor agglomeration in promoting firms' new quality productive forces increases. Heterogeneity analysis shows that the effect of data factor agglomeration on new productivity of enterprises is more significant in non-state-owned enterprises, technology-intensive enterprises, high-tech industries and regions with better digital infrastructure. The findings provide insights into how to utilize new factors of production to cultivate new productivity.

New energy vehicle industry is an important strategic emerging industry for cultivating national new quality productive forces. Based on patent application data from 2013 to 2022 of the three enterprises (BYD, GEELY and WULING), Lotka-Volterra population competition model was constructed to explore out their coopetition relation mode and evolving trends. Main conclusions are as follows. Firstly, coopetition relation has been an important driving factor to stimulate technological innovation evolution. Secondly, their coopetition model shows great heterogeneity among the three leading enterprises. At current stage, "BYD + GEELY” “WULING + GEELY" show such mutually promoting type. Meanwhile, " BYD +WULING" show the competition-cooperation type. Thirdly, in accordance with simulation results, innovation outputs of enterprises vary with coopetition relation coefficient changes. Innovation output effect of new vehicle industry is positive when the coopetition intensity of mutually-prompting type grows. The positive innovation effect from "WULING + GEELY" coopetition intensity growth is more than that from the other one. Therefore, cultivating orderly competition and coordinated cooperation mode mechanism, especially most potential competition types, should be paid more focus on realizing its high-quality innovation outputs and supporting its new-quality productivity formation for the new energy vehicle industry of China.

In the era of digital economy, digital technology innovation has become a core factor driving China's economic development. Based on the data of China's A-share listed companies and the list of the top five suppliers and distributors of listed companies from 2009 to 2022, the impact and mechanism of digital innovation in upstream and downstream enterprises of the industrial chain on digital innovation in midstream enterprises. By combining international patent classification with text analysis method, digital innovation patents was empirically investigated to characterize the level of digital innovation. The results show that the improvement of digital innovation levels in upstream and downstream enterprises significantly promotes digital innovation in midstream enterprises, indicating a significant positive spillover effect in the industrial chain. This effect is more significant in upstream enterprises, enterprises in the same industry, and enterprises with the same ownership type. Technological knowledge diffusion and market performance incentives are the two channels for the spillover effect. The micro-mechanisms of spillover effects among upstream, midstream, and downstream enterprises within industrial chains are revealed. Theoretical references and practical insights are provided for promoting digital innovation and key technological breakthroughs in enterprises, as well as facilitating the integration of the digital economy with real economy along industrial chains.

With the deepening of globalization, latecomer firms continue to emerge in the market and are gradually evolving from innovation catch-up to innovation frontier. Although there are already latecomer firms that are fully developed and gradually located in the leading position in the innovation ecosystem, there is little research on the mechanism of the dynamic process of catching up with the leading firms based on the same innovation ecosystem in the context of innovation catching up. By analyzing the longitudinal case study of the fire-fighting enterprise Tanda, the analytical framework of "catching-up pressure-catching-up process-catching-up result" was built. It is found that in the ecological integration stage, the three windows of opportunity, namely, technology, system and market, comprehensively identify and guide enterprises to adopt different strategic responses, which expands the research on the coupling of windows of opportunity and strategy. In the innovation catching-up stage, the evolution of innovation is characterized by a progressive trend from "imitation innovation-independent innovation-collaborative innovation". The integration of innovation ecology shows a gradual deepening process from "embedding" to "evolution" to "stabilization", and realizes its own ecological position in the innovation ecosystem. It is help to provide reference ideas for further research on the theory of window of opportunity, to further improve the research on catching up of latecomer enterprises in China, and to expand the theoretical research system of innovation ecosystem.

The incubator has played a significant role in driving the formation of local entrepreneurial ecosystems, revitalizing regional advantages, and establishing sustainable development models with regional characteristics. By focusing on Hongtai Zhizao, a case study was conducted to analyze how the incubator facilitates the evolution of the entrepreneurial ecosystem centered around it. The dynamic coupling and interaction between ambidextrous capacity in the development process of the incubator was identified. It is found that the evolution of the entrepreneurial ecosystem involves three stages. The specific mechanisms through which structural, environmental, and leadership ambidextrous capacity influence the progression of the entrepreneurial ecosystem were examined. From a dynamic perspective, the typical configurations of these three ambidextrous capacity are summarized, clarifying their interactive coupling relationships and the bidirectional interaction between external environments and internal structures. The findings contribute to understanding how incubators drive the evolution of entrepreneurial ecosystems and enrich the research on the coupling architecture of ambidextrous capacity.

Different fresh-keeping efficiency of fresh e-commerce platform leads to differences in product freshness and price, which affects consumers' purchase decisions. At present, the competition in the fresh e-commerce market is fierce and the operation is chaotic. In order to scientifically guide the healthy development and operation of the fresh e-commerce platform, the competition model of the duopoly fresh e-commerce platform was constructed based on Hotelling. The product freshness competition and price game of the two fresh e-commerce platforms under the influence of fresh efficiency were studied, and the effects of consumer characteristics and preservation efficiency on the competition game of the two fresh e-commerce platforms were discussed. The results show that the fresh e-commerce platform with high fresh-keeping efficiency has low unit fresh-keeping cost, and the strategy of providing high-freshness and high-price products is effective, and vice versa. For fresh products, no matter what the consumers' characteristics are, the low-price strategy can not effectively seize the market share. therefore, the fresh e-commerce platform should improve its fresh-keeping efficiency, reduce the fresh-keeping cost, and effectively profit from the high-tech freshness and high-price product strategy.

Under the "manufacturing power" strategy, enterprise innovation, particularly design innovation, plays a crucial role in transforming China from a manufacturing powerhouse to an innovation-driven economy. However, enterprise design innovation is characterized by a short research and development cycle, quick results, low investment, and minimal risk. Enterprises also exhibit a tendency towards short-term profit-seeking in their design innovation practices, often neglecting long-term objectives. Although research in this area is emerging, a systematic literature review is still lacking. First co-citation analysis theory was used to screen and 518 articles published from 1990 to 2023 based on the subject search terms "enterprise design innovation" and "enterprise innovation design" in CNKI (China National Knowledge Infrastructure) were reviewed. Knowledge mapping and visual analysis techniques were applied to construct visual maps and the progress, hot topics, and future trends in enterprise innovation design research was analyzed. Secondly, the research on the paths of enterprise design innovation driving business development, technological innovation paths, paths of autonomous and collaborative innovation, and their underlying mechanisms were systematically summarized, as well as the driving mechanisms and practical paths. Furthermore, the challenges faced by enterprise design innovation in China were critically discussed, and research gaps and issues were identified. Finally, placeing enterprise design innovation in the era of technological convergence and cross-disciplinary integration, future research directions was proposed. Future research on enterprise design innovation in China should focus on interdisciplinary, cross-field, and cross-regional collaborative studies, comparative research from a global perspective, and integrated studies combining macro and micro-level analyses. It clarifies the growth trajectory and development direction of enterprise design innovation in China and provides valuable references for related research based on China s innovation practices in manufacturing.