In order to guide and promote the trading and circulation of data elements, and promote the healthy development of the data trading market, a new quality productive forces "new quality" analysis framework was constructed, and based on this framework, the new quality characteristics of data elements and market development laws were analyzed in depth. The factors that restrict data trading and smooth circulation were deeply explored. The study showed that the trustworthy mechanism is a key link affecting data trading circulation, and credit management for data trading is the future development direction. This system draws on mature experience in credit supervision in other fields, aiming to guide the behavior of all parties involved in data transactions through credit management systems, and solve the problems of insufficient and irregular development of the data element market. The results indicate that building a credit management system for data transactions can ensure the fairness and transparency of on exchange transactions, guide more data transactions to be conducted on the exchange, reduce the risks caused by information asymmetry in off exchange transactions, enhance trust between trading parties, reduce fraudulent behavior, help promote the trustworthy trading and circulation development of data elements, and provide institutional guarantees for the efficient development of new productive forces.

Resistance to change plays a key role in the success or failure of organizational change, and most studies regard change resistance as a negative change reaction behavior. Recently, some scholars believe that resistance to change has not only negative side, but also positive side. However, there is no relevant measurement tool to represent the full connotation of resistance to change, which will affect the academic and practical circles' comprehensive understanding of resistance to change. Based on grounded theory, the literature on resistance was combed to change and open interviews with enterprise workers was conducted, the connotation and structure of resistance to change in depth was exploved, and a resistance to change scale was developed. And through the content validity, structure validity and criterion validity of the resistance change scale, the measurement scale was revised and verified. The results show that resistance to change consists of two dimensions, individual orientation and organization orientation, with a total of seven items. In addition, further empirical research is conducted to test the differentiated impact of individual-oriented and organization-oriented resistance to change on turnover intention. It is clarified the connotation and structure of resistance to change and developed a measurement scale of change resistance, which provided a reference and measurement basis for expanding the theory of organizational change and condensing the behavior law of change response.

The dual goals of green financial policies are to direct funds into green industries and promote the transformation of polluting enterprises. A differenceindifferences model was utilized to examine how the Green Credit Guidelines, introduced in 2012, affected the performance of polluting enterprises. Data from 968 nonfinancial listed companies on the Ashare market in China between 2004 and 2017 were analyzed. The Green Credit Guidelines are found to reduce both the shortterm and longterm performance of enterprises in heavily polluting industries. This negative impact is achieved through the channels of financing constraints, investment constraints, and internal control quality effects. After the implementation of the Green Credit Guidelines, the scale of investment by polluting enterprises in fixed assets, intangible assets, and other longterm assets decreases. The financing constraints of polluting enterprises measured by the SA index tighten. The management status of polluting enterprises measured by the internal control quality index deteriorates. Heterogeneity analysis shows that the Green Credit Guidelines has a more obvious impact on the performance of stateowned polluting enterprises and polluting enterprises in areas with low marketization and weak environmental regulation. Reliable evidence is provided on the impact of green credit on the economic performance of enterprises at the micro level, and policy insights are given on how green finance policies can aid in the green transformation of polluting enterprises.

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.