Big data technologies have caused the volume, velocity, and variety of data generated by enterprise information systems to grow exponentially. It has thereby rendered traditional approaches to data management no longer effective. This paper focuses on data storage, management, and analysis strategies for information systems within big data environments and searches for pathways to maximize value. Employing a combined literature review and case study methodology, it systematically reviews existing data management frameworks while integrating corporate practical experience through case studies. The findings indicate that data management activities of an existing information system experience problems, like storage resource management efficiency, the lack of standardized data governance, and inefficient use of analytical technologies. The proposed strategy involving data management hierarchies, data governance, and intelligent analytics can significantly increase the capacity of an information system in creating value from existing data. The research is beneficial and can offer significant help to existing and running enterprises on how they can improve data management practices and gain a competitive edge.

This paper aims to explore the current status and emerging trends of corporate supply chain management within the context of intelligent supply chains by conducting a comparative analysis of the supply chain systems employed by three typical high-tech enterprises, namely Apple, Xiaomi, and Huawei. Adopting a case analysis method, the study systematically examines the theoretical foundations of intelligent supply chains while providing a detailed analysis of the distinctive features and strategic approaches that characterize the supply chain management practices of these three corporations. The findings indicate that Apple has significantly enhanced production efficiency and reduced inventory by implementing a global outsourcing strategy and building an agile and efficient supply chain network. Huawei has remarkably improved operational efficiency and market competitiveness by focusing on digital and intelligent upgrades and constructing a high-end supply chain system integrating intelligent procurement, manufacturing and logistics. Xiaomi has achieved rapid market response and cost control by adopting a hybrid and agile collaborative supply chain model based on ecological cooperation and digital empowerment. The study concluded that the evolution of intelligent supply chains presents both unprecedented opportunities and challenges for corporate supply chain management.

With the continuous development of logistics and online shopping, Cainiao Station, as the last link of the logistics system and an important facility for convenient public services, is gaining increasing importance in urban daily life and industrial development. However, it still has some obvious problems that seriously affect its operational efficiency. This paper uses the methods of ergonomics, facility planning and operations research to analyze the existing problems from the aspects of distribution efficiency, station location design, spatial layout and utilization rate, and user pick-up experience, and puts forward feasible solutions one by one, so as to improve the operational efficiency of the station and the user pick-up experience. Finally, by observing the common problems and operational status of some Cainiao Stations and combining the actual situations of different stations, this paper proposes a set of universally applicable and highly practical optimization templates to improve the operational efficiency and customer satisfaction of Cainiao Station.

The objective of this study is to examine the predictive relationship between the China stock market and the bond market. With the worsening economic environment entering 2024-2025, the stock-bond linkage mechanism has played an increasingly important role in financial forecasts. But through the current forecasts, it has been noticed that the forecasts often ignore the systematic connection of market information between the markets. The construction of the model was focused on a VAR model with the return of the stock market as an exogenous variable, which was contrasted with the traditional ARIMA model, and the nature of the connection between the two markets was evaluated through Granger causality test, variance decompositions, and impulse responses. The outcome of the test showed the superiority of the VAR model in terms of the key predictive accuracy of the forecast, especially regarding the declining trend of the bond market yields. Overall, the findings suggest that the stock market exhibits significant leading and explanatory power over the bond market, indicating that integrating cross-market information can enhance forecasting accuracy and provide more effective quantitative support for asset allocation and risk early warning by leveraging leading indicators from the stock market.

Based on Bourdieu's 'capital-field-habitus’ theoretical framework, this paper conducts a secondary analysis of three empirical studies in the Chinese educational context to illustrate how family cultural capital influences students' academic performance through academic habitus. The results show that there is a stable positive relationship between family cultural capital and academic performance, which is not mainly achieved in a direct way, but through the formation of habitus habits that are consistent with the school assessment rules, which are further manifested in sustained learning behaviours, and finally transformed into academic strengths. In the test score-centred Chinese educational environment, learning habits play a key mediating role in the transformation of cultural capital into academic performance, and the differences in the intensity of the role in different studies are related to differences in educational stages and research designs, and reflect the process by which the educational evaluation mechanism and the structure of learning practices jointly influence the path of capital transformation.

This study adopts a research design of applying the two-way fixed effects.model in the market to examine whether ESG dimensions have a different influence on the costs of corporate financing and the mechanism by which the policy is transmitted using non-financial A-share listed companies between 2015 and 2024 as a research sample. It empirically studies the cross-effect of the ESG environmental (E), governance (G) and social (S) variables on the corporate financing costs, alongside the environmental policy, monetary policy and regulatory policy. The findings show that the three ESG dimensions have a substantial negative impact on the financing cost with the strongest effect found in governance. The cross-effect analysis shows that a tight environmental policy increases the influence of the environmental dimension on the costs reduction, accommodative monetary policy increases the positive effect of the governance dimension, and the intensive regulatory influence increases the worth of the social dimension. The three hypotheses are all proven. The results inform the heterogeneous policy channels, on which the ESG can have impact in reducing financing costs and gives firms information to design differentiated ESG strategies.

This paper takes the resettlement work of urban redevelopment and renewal projects such as urban villages as an opportunity to explore the trial implementation of a housing voucher system. Against the backdrop of urban village redevelopment, this paper first analyzes the current situation of urban village redevelopment in Beijing, focusing primarily on the shortcomings of the existing resettlement policies. Next, it examines how housing voucher rules affect the real estate market in representative cities, highlighting the pros and cons of the current system. The system's application in Beijing, taking into consideration the city's particular characteristics, is discussed, along with policy recommendations. This study improves mega-city housing voucher research and gives a novel urban village rehabilitation and relocation perspective. Beijing can use the research to improve urban village rehabilitation, relocation, residential property destocking, and mega-city concerns.

The close price of the Growth Enterprise Market reflects the economic environment and market risk appetite. Since it is composed of growing companies such as technology and biopharmaceuticals, a rising closing price implies higher profit expectations in these startups, looser market liquidity, and a greater preference for market risk. In order to solve the issues mentioned above, this paper will apply the traditional ARIMA model and STL decomposition, respectively, processing ChiNext data from 2015 to 2025 after the transformation of Box-Cox. Employ ETS model in the trend and the ARIMA-GARCH model in the remainder which has heteroscedasticity. Compare difference before and after using the SNAIVE method in the seasonal portion in terms of weak seasonality. After calculating the economic effect, the hybrid STL-ETS-GARCH model achieved the highest strategic Sharpe ratio (0.876) and positive excess Sharpe ratio (0.212) in the out-of-sample testing, with its directional accuracy (49.2%) and profit-loss ratio (1.22) demonstrating robust performance. Although the STL-ETS-GARCH-SNAIVE model recorded the highest directional accuracy (51.3%), its Sharpe ratio (0.018) was significantly deteriorated due to the inclusion of redundant seasonal components. The ARIMA model delivered moderate performance. The paper raises a more elaborate method on the condition that seasonal components are handled prudently.

The explosive growth of e-commerce has placed significant demands on logistics services, particularly in terms of response speed, accuracy, and cost control. Traditional warehousing models are no longer sufficient to meet the increasing requirements. Artificial intelligence technology has become an inevitable choice for the industry to build core competitiveness. This paper reviews recent literature to explore how artificial intelligence can reshape modern e-commerce distribution centres. By analysing the transformative application of machine learning, computer vision, and robotics in practice, the study shows that artificial intelligence is transforming distribution processes, moving from manual labour to algorithm-driven workflows. Through accurate demand prediction, automated warehouse operations, dynamic route optimisation, and intelligent decision-making, AI transforms distribution centres from cost centres to value generators, thereby enhancing supply chain agility and intelligence. Despite challenges such as high initial investment costs and complex system integration, the deep integration of artificial intelligence and technologies such as the Internet of Things is accelerating industry-wide advancement. By constructing the framework of "process deconstruction-technology application-mode reshaping", this study provides a theoretical perspective for the evolution of intelligent logistics and demonstrates the significance of artificial intelligence in reshaping the modern retail logistics mode.

This paper examines whether stock borrowing costs contain information about daily equity returns beyond standard risk factors. The study merges daily U.S. stock returns from CRSP with daily borrowing costs from Markit using a harmonized CUSIP8 identifier, and adds daily Fama-French factors from the Kenneth R. French Data Library. The study constructs a daily borrowing-cost factor by going long high-borrow-cost stocks and short low-borrow-cost stocks, rebalanced each day. In firm fixed-effects panel regressions of stock excess returns on the Fama-French three factors and the borrowing-cost factor, the loading on the borrowing-cost factor is positive and highly statistically significant. The results suggest borrowing costs are systematically related to daily excess returns, consistent with the idea that short-selling frictions are relevant for asset pricing. This finding fills the gap in the exploration of short-selling related friction factors in short-term asset pricing research, and also provides new empirical evidence on the daily dimension for understanding the linkage between micro transaction costs and stock returns, which has important reference value for the improvement of asset pricing theory and the formulation of actual investment strategies.