In this paper, we explore the emerging role of graph neural networks (GNNs) in optimizing routing for next-generation communication networks. Traditional routing protocols, such as OSPF or the Dijkstra algorithm, often fall short in handling the complexity, scalability, and dynamic nature of modern network environments, including unmanned aerial vehicle (UAV), satellite, and 5G networks. By leveraging their ability to model network topologies and learn from complex interdependencies between nodes and links, GNNs offer a promising solution for distributed and scalable routing optimization. This paper provides a comprehensive review of the latest research on GNN-based routing methods, categorizing them into supervised learning for network modeling, supervised learning for routing optimization, and reinforcement learning for dynamic routing tasks. We also present a detailed analysis of existing datasets, tools, and benchmarking practices. Key challenges related to scalability, real-world deployment, explainability, and security are discussed, alongside future research directions that involve federated learning, self-supervised learning, and online learning techniques to further enhance GNN applicability. This study serves as the first comprehensive survey of GNNs for routing optimization, aiming to inspire further research and practical applications in future communication networks.

China’s retail industry faces unique challenges in supply chain financing, particularly for small and medium-sized enterprises (SMEs) that often struggle to secure loans due to insufficient credit ratings and collateral in the business environment of China. This paper presents a groundbreaking approach that integrates real-time data elements into financing models, addressing the critical issue of information asymmetry between financial institutions and retail SMEs. By leveraging dynamic data such as orders, receivables, and project progress, our novel framework moves beyond the limitations of traditional asset-based lending, employing advanced data analytics for enhanced credit assessment and risk management. Applying the Stackelberg game theory, we explore the strategic interactions between suppliers and purchasers in the retail supply chain, identifying optimal financing strategies that improve capital flow efficiency and reduce overall costs. Our comprehensive data-driven model incorporates various scenarios, including the traditional supply chain financing model (Model T) and the innovative data-element secured financing model (Model G). The latter further considers risk assessment, risk appetite, volume, and schedule factors, providing a holistic approach to financial decision-making. Through rigorous mathematical modeling and numerical analysis, we demonstrate the effectiveness of our proposed framework in optimizing supply chain financing strategies. The results highlight the potential for data-driven approaches to unlock new financing opportunities for SMEs, fostering a more collaborative and efficient ecosystem within the retail industry. This study presents comprehensive data-driven strategies that unlock new financing opportunities for SMEs, providing a practical roadmap for stakeholders to foster a more collaborative and efficient supply chain financing ecosystem. The significance of studying supply chain finance for small and medium-sized enterprises (SMEs) lies in optimizing financing models to address the financing difficulties faced by SMEs. This helps improve their market competitiveness and promotes resource sharing and collaboration among all parties in the supply chain, thereby achieving sustainable economic development.

Research on family businesses has focused on the differences between family and non-family firms regarding the top management team (TMT), while this study further explores the difference within family firms from different regional culture based on the perspective of socio-emotional wealth (SEW) and evolutionary psychology. Using a sample comprised of all 625 family firms listed on the Small & Medium Enterprise Board and Growth Enterprise Board in Shenzhen Stock Exchange, this study finds that in regions of strong clan culture, family members are more willing to be involved in a family business and accept lower economic rewards. Particularly, when financial risk is high, these relationships mentioned above become more significant. Based on the results, this study proves that regional culture can affect the characteristics of top management teams in family firms, explains the heterogeneity of family firms’ decision-making from a culture-based perspective and extends existing research on family business from the level of “family firm vs. non-family firm” to that of “family firms affected by different regional cultures”.

Existing studies have suggested that nonfamily CEOs are more likely to be fired from family firms, while we focus on why family CEOs are also fired from family firms. Using data from 455 listed Chinese family firms, we find that family CEOs with affinity ties are more likely to be dismissed as they are not genetically related to the family. The difference becomes greater when firm performance is poor or family ownership is high. These findings elaborate that business-owing family is not a group with aligned interests, that is, family members with different family identities are treated differently within family. Besides, existing studies have emphasized that the preservation of socioemotional wealth in family firms can affect firms’ operations, while this study further proposes that the preservation of socioemotional wealth can also have an impact on the business-owning families themselves.

It has been generally believed that the major shareholders of family firms are more willing to implement egoistic behaviors aimed at benefiting the family. This study analyses whether the major shareholder of the family firm whose name contains “Confucian symbols” such as benevolence, righteousness, loyalty, and kindness will reduce family self-interested behaviors as his name indicates. Using a sample consisting of all 425 family firms listed on Small and Medium Enterprise Board and Growth Enterprise Board, the result shows that the major shareholder whose name contains Confucian symbols is less likely to misappropriate corporate assets and less likely to make “family-first” personnel arrangements, meanwhile is more open to external supervision. Further mechanism testing reveals that the major shareholder whose name contains Confucian symbols also tends to choose corporate culture that reflects Confucianism. The study confirms that the name of the major shareholder is one of the factors which can affect the operation of the family business, demonstrates that different family firms have different attitudes towards family self-interest, and promotes the extant research from the “differences between family and non-family firms” level further to the “differences among family firms running by different families” level.