Supply chain management (SCM) is pivotal in orchestrating the flow of goods and services from suppliers to consumers, fundamentally shaping business operations worldwide. However, traditional SCM faces significant limitations, such as inefficiencies in handling complex data structures and adapting to rapid market changes, which undermine operational effectiveness. The application of deep learning technologies in SCM is increasingly recognized as crucial, offering powerful tools for real-time visibility, predictive analytics, and enhanced decision-making capabilities. We propose a VAE-GNN-DRL network model that integrates Variational Autoencoder (VAE), Graph Neural Network (GNN), and Deep Reinforcement Learning (DRL) to address these challenges by efficiently processing and analyzing complex supply chain data.

This study examines the impact of generative artificial intelligence (GAI) on Chinese enterprises. 320 participants completed electronic surveys, revealing a positive relationship between GAI and corporate performance. Managerial relationships were found to play a crucial role, mediating the influence of GAI on performance. Additionally, the speed of technological change in the industry was identified as a moderator, highlighting the dynamic nature of GAI's impact on managerial relationships and firm performance. These findings underscore the strategic importance of GAI in cultivating managerial relationships and improving enterprise performance, deepening our understanding of the integration of advanced AI technologies and management practices in the Chinese market. Practical implications are offered for decision-makers in technology-intensive industries, providing valuable insights for leveraging GAI to gain a competitive edge and achieve sustainable growth in a rapidly evolving technological landscape.

Personalized recommendation systems have become crucial for enhancing user experience and driving engagement in various online platforms. However, existing methods face challenges in accurately modeling high-order user-item relationships, dynamically capturing user preferences, and effectively utilizing multi-modal data. These limitations hinder their ability to deliver relevant, diverse, and context-aware recommendations. To address these challenges, we propose the Graph Attention-based Dynamic Recommendation Framework (GADR). GADR incorporates a graph attention mechanism to prioritize high-order relationships dynamically, a dual-channel structure to simultaneously model long-term and short-term user preferences, and a unified pipeline for integrating textual, visual, and behavioral data. By combining these components, GADR ensures adaptability to dynamic user behavior, improves recommendation diversity, and enhances ranking quality.

In the new situation of building an entrepreneurial ecosystem, faced with the global multi-agent competition of the system, how new ventures should integrate with the system, isomorphism, and use their own advantages to cultivate new venture resilience has become an important global issue. The purpose of this study is to explore the combined impact of digital transformation, entrepreneurial ecological orientation, and organizational structure on the resilience of new ventures. Based on qualitative comparative analysis (QCA), this study systematically explores the significant key conditions and configuration effects that affect the cultivation of new venture resilience. There are four types of configurations that help new ventures to cultivate resilience, namely: strategic change, flexible response, leadership niche and institutional logic. This study explores how these three dimensions interact to achieve high firm resilience, ultimately contributing to the research of resilience from the perspective of the entrepreneurial ecosystem.

As the global market continues to evolve and competition escalates, the business environment becomes increasingly competitive. How manufacturing companies improve their competitiveness has always been a topic of great concern. Service transformation and product innovation are key factors and are considered to be important ways for enterprises to stand out in the market. Traditional service transformation and product innovation processes often face complex problems, including the diversity of customer needs and fierce market competition. This makes it difficult for companies to accurately capture market opportunities, provide personalized solutions, and respond quickly to changes. At the same time, many companies also face problems with product quality control and production efficiency, which further weakens their competitiveness. It is against this background that the importance of computer vision technology has become increasingly prominent.

In the current context of rapid technological advancement, smartphones have become an indispensable part of people's daily lives. This has led to an increasing focus on the satisfaction of consumers with smartphone products, as understanding consumer emotions and satisfaction has become a key factor for manufacturers and retailers to enhance the quality of products and services. This study delves into the satisfaction of consumers with smartphones in the market through an in-depth application of text mining techniques, leveraging advanced technologies such as natural language processing, sentiment analysis, and topic modeling. Our research methodology encompasses the process of collecting and preprocessing a substantial volume of consumer reviews from online shopping platforms. Subsequently, we apply Latent Dirichlet Allocation (LDA) for topic modeling and Extreme Learning Machine (ELM) for sentiment analysis.

Digital entrepreneurial success has gained attention because the development of digitalization has brought multiple opportunities for entrepreneurs along with many challenges. While prior research has primarily examined the determinants of success at the founder and organizational levels, the impact of venture groups has been neglected, resulting in a lack of exploration of the group effect. In this study, we apply social learning theory to investigate how lead entrepreneur's digital leadership influences digital entrepreneurial success. By analyzing data from 88 digital entrepreneurial teams, we found that lead entrepreneur's digital leadership is positively related to entrepreneurial team's digital entrepreneurial success. Likewise, lead entrepreneur's digital leadership positively correlates with digital entrepreneurial success through technology absorptive capacity and technological innovation capability. Moreover, we argue that leader group prototypicality not only strengthens the impact of lead entrepreneur's digital leadership on the entrepreneurial team's technology absorptive capacity but also amplifies its influence on the entrepreneurial team's technological innovation capability. Our results provide contributions to both the entrepreneurship and leadership literature.

Along with the rapid development of digital information technology, e-government is of great potential because it is a new form of conducting public administration and a way of demonstrating governmental innovation. The literature suggests that foreign direct investment (FDI) is increasingly associated with the continuing development of e-government in China. Using the Annual Census of Industrial Enterprises and e-government scores of government portals, this study examines the effects of e-government on FDI and how government subsidies mediate relationships between e-government and FDI. Our results show that e-government positively affects FDI, and government subsidies have a positive effect on foreign enterprises' investment efficiency by playing a mediating role between e-government efficiency and FDI. The findings make both theoretical and practical implications related to the role, provision and acquisition of government subsidies; e-government systems and FDI; e-government scores and government efficiency; and firms maintaining focus on areas that are government priorities.

As a common standard for global business reporting, eXtensible Business Reporting Language (XBRL) can make up for the deficiencies of traditional financial reports in terms of standardized disclosure and information use costs, and provide firm-specific information to report users, reduce the level of corporate stock price synchronicity, and then improve capital market information allocation efficiency. Based on the financial data of Chinese listed companies from 2005 to 2011, this paper mainly focuses on the impact of XBRL adoption on stock price synchronicity.

Dempster-Shafer evidence theory has been widely used in many applications due to its advantages with weaker conditions than Bayes probability. How to measure the uncertainty of basic probability assignment (BPA) in Dempster-Shafer evidence theory is an open and essential issue. Tsallis entropy as nonextensive entropy proposed according to multifractals has been used in many fields. In this paper, a new uncertainty measure of BPA is presented based on Tsallis entropy. The key issue is to determine the value of q in Tsallis entropy. In addition, this paper also analyzes the properties of proposed uncertainty measure. Some numerical examples are used to illustrate the efficiency of the proposed method. Finally, the paper also discusses the application of the proposed method in decision-making.

Factors affecting online businesses are complicated and intertwined. To stimulate online sales, understanding of key influential factors and causal relationships among the factors is important. This paper introduces a hybrid method, called the DANP method, to expose the driving factors that most influence the online businesses. A hierarchy of fourteen criteria in six dimensions is formed to denote the influence levels and weights of factors. The causal graph shows that “Uncertainty” dimension has the highest degree of direct impact on other dimensions; thus, strong efforts should be made to eliminate the influence of this dimension throughout the online shopping process. In addition, the results also show “perceived price” to be the greatest important criterion among all; thus, it should be treated with top priority to boost the online businesses.