The value alignment of large language models is a global issue related to ensuring safe collaboration when enterprises and societies adopt these technologies. Achieving alignment between the behavior of large language models and the value intentions of decision-makers as well as societal norms is identified as the core challenge for ensuring safety and trust. Formal rationality and substantive rationality, two philosophical concepts proposed by Max Weber, were introduced to explore value alignment mechanisms. Four value alignment states in enterprise management were categorized including "high formal rationality-low substantive rationality" as technical drift, "high substantive rationality-low formal rationality" as value prioritization, "low formal rationality-low substantive rationality" as alignment failure, and "high formal rationality-high substantive rationality" as dynamic alignment. Transparency, clarity, and sociality were identified as analytical standards for value alignment. Pathways to achieve value alignment in enterprise management were proposed, including the embodiment of cognitive capability in the "technical drift→dynamic alignment" pathway, the clarification of technical intentionality in the "value prioritization→dynamic alignment" pathway, and the construction of meaning in the "alignment failure→dynamic alignment" pathway. The findings provide theoretical support and practical insights into the value alignment mechanisms of large language models in enterprise management.

As a long-term state policy, talent strategy is of great significance to national development and regional construction. The key to the construction of a high-quality talent system is to optimize the development environment for scientific and technological innovation, so as to achieve an increase in the sustainable efficiency of talent introduction. Based on this, the DPSIR-DEA-Malmquist index model was built. The 31 Chinese provincial (Due to the lack of data, the statistical data mentioned here do not include the Hong Kong Special Administrative Region, the Macao Special Administrative Region and Taiwan Province.) talent introduction activities from 2014 to 2020 were taken as the research object, and the sustainable efficiency of talent introduction was analyzed. Further, considering the practical significance of the technological innovation climate for talent introduction activities, the threshold regression was then used to analyze its impact mechanism. The results show that the sustainable efficiency of provincial talent introduction in China has been steadily demonstrated and continuously enhanced, and the regional differences have been significantly narrowed in both the geographical and spatial and temporal levels. The decomposition of Malmquist index in the level of technical efficiency shows an "N-shaped" trend, which is basically relatively consistent with the trend of Malmquist index. However, the index of technological progress shows a trend of "M-shaped", indicating that it is not the upward driving force for the improvement of the sustainable efficiency of talent introduction. The role of technological innovation climate in talent introduction of all stages has been tested by the threshold effect of technical efficiency. When the technical efficiency crosses the threshold of 1.775, scientific research service personnel, national policy-based education funds and continuous funds all have a significant and positive impact on the sustainable efficiency of talent introduction. Therefore, the current situation of emphasizing talent recruitment over utilization should be changed. The construction of the scientific-tech innovation atmosphere should be intensified. The sustainable efficiency of talent introduction should be given full play. These are regarded as the keys to reversing the Matthew effect of the talent strategy and promoting the regional scientific development.

The impact of artificial intelligence (AI) on the labor market, based on the Routine-Biased Technological Change paradigm, is widely acknowledged. However, existing job classification methods lack detail and accuracy. To address this limitation, the Chinese-BERT-wwm model was optimized to classify recruitment data from listed companies between 2013 and 2019 into routine and non-routine jobs, achieving a test set accuracy of accuracy of nearly 93%. Additionally, the GLM4 model was used to match job titles and descriptions to the "Chinese Occupational Classification (2022 Edition)" to identify digital occupations and analyze the impact of AI technology on labor demand structure. Empirical results show that higher AI technology levels significantly increase demand for non-routine jobs and reduce demand for routine jobs, with pronounced effects in non-state-owned enterprises, high-tech industries, and manufacturing. Further analysis reveals that the increased demand for non-routine jobs is primarily driven by growth in non-routine cognitive positions. Mechanism analysis shows that AI adoption increases non-routine job demand through productivity effects and the creation of new digital occupations, while reducing routine job demand through substitution effects. It expands the application of large language models in economic text analysis.

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.

In order to continuously do a good job in talent work,and further build a talent strong country with high quality, and achieve high-level technological self-reliance and self-improvement, in recent years, especially since the 18th National Congress of the Communist Party of China, the relevant functional departments of the country have successively issued many talent policy documents to promote the gradual optimization and improvement of the talent policy system in China. Based on the systematic review of 808 talent policy texts released at the national level since the 18th National Congress of the Communist Party of China, a three-dimensional analysis framework of "ideas-goals-tools" talent policy was construsted. Then, based on the LDA topic generation model, methods such as word frequency analysis and topic analysis for in-depth investigation were comprehensively used. It can be discovered that China's talent policy embodies the unity of principle, planning, and operability in the conceptual dimension, emphasizes systems and institutional mechanisms reform and clear feasibility in the goal dimension, and highlights flexibility, practicality, and collaborative innovation in the tool dimension. Overall, the systematic features of talent policy are quite obvious.Based on these results, it is suggested that the core concept of“talent is the first resource”should be adhered,efficient and clear policy objectives should be set, a balanced combination of policy tools should be constructed,promoting the linkage and cooperation between policy concepts, policy objectives, and policy tools should be accentuated at the same time.

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.

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.

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.

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.

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.