In the traditional logistics industry, asymmetric information, issues with goods’ authenticity, and insufficient security can hinder business transactions, which induces high operating costs and is time-consuming. With its decentralisation, transparency, high security, and resistance to tampering, blockchain technology can effectively solve these issues. In this study, we designed a two-stage analysis scheme to identify and summarise how blockchain has been applied in modern logistics information sharing and its challenges. The latent Dirichlet allocation topic model was employed with 2465 selected studies to summarise the application scenarios. These data were used to create a blockchain integration framework to determine the optimal use of blockchain in modern logistics information sharing. Six key areas were identified: supply chain finance, logistics tracking, logistics collaboration, process optimisation, data security management, and logistics business mode innovation. Then, three typical real-world cases were analysed, and managerial recommendations to boost blockchain adoption in this industry are proposed.

Obtaining a multi-resource allocation scheme for multi-task influenced by uncertain factors is a critical problem in collaborative logistics network. This paper presents an optimal allocation model of fuzzy resources for multi-stage random logistics tasks based on the six-point trapezoidal fuzzy number and the membership function. Besides, considering task demands and resource constraints, a new cost-time-quality multi-objective programming of N-N task-resource assignment is introduced, which can be divided into minimize total logistics cost and execution time, maximize total service quality. Furthermore, by setting the different simulation scenarios, the results show that if the decision maker has a higher risk preference and pursues the optimization of single or multi-objective, the higher degree membership and satisfaction function values can be gotten with a larger compensation coefficient. The allocation scheme of task-resource assignment generated by proposed model has a high global level of utilization efficiency, which can effectively utilize fuzzy resources in collaborative logistics network, and avoid resource shortage caused by the excessive occupation of local resources.

Amidst recent supply chain disruptions triggered by pandemics and crises, the imperative of bolstering supply chain security and resilience is escalating. Central to this endeavor is the augmentation of risk-taking capabilities among enterprises at supply chain nodes. Rapid advances in artificial intelligence (AI), coupled with the burgeoning concentration within supply chains present promising avenues for enhancing corporate risk-taking capacity (CRTC). However, a conspicuous gap exists in the relationship between enterprise AI transformation, supply chain concentration, and CRTC. This study constructs a panel simultaneous equation model to contrast the direct impact of corporate AI transformation on CRTC with its indirect influence, facilitated by the reduction of supply chain concentration. The results indicate that the overall effect of corporate AI transformation on CRTC is positive. In the indirect path, an increase in supply chain concentration effectively enhances CRTC, and firms exhibiting higher CRTC also show a preference for supply chains with centralized configurations. All of these interactions involve heterogeneity in property rights and firm life cycle stages. This study broadens the understanding of factors influencing CRTC at the supply chain level and sheds light on the policy implications of enterprise AI transformation. It offers valuable insights for corporates in shaping their risk management strategies and contributes to the discourse on the advancement of emerging technologies and supply chain practices.