Purpose: This paper aims to develop a coordination mechanism that can be applied to achieve the channel coordination and information sharing simultaneously in the fresh agri-food supply chain with uncertain demand. It seeks to elucidate how the producer can use an option contract to transfer the risk caused by uncertain demand, impel the retailer to share demand information and improve the performance of supply chain.Design/methodology/approach: An option contract model based on the basic model of fresh agri-food supply chain is introduced to compare the production, profit, risk and information sharing condition of the supply chain in different cases. In addition, a case study focusing on the sale of autumn peaches produced by a local producer is investigated, which provides evidence of the applicability of the authors’ approach.Findings: The optimal option contract can help the supply chain achieve channel coordination and reach Pareto improvement. In the meantime, such a contract will encourage the retailer to share market demand information with producer spontaneously and help maintain the strategic cooperation between two parties.Research limitations/implications: This paper considers a single-producer, single-retailer system and both of them are risk neutral.Practical implications: Presented results can be used as suggestions for improving the contract design of fresh agri-food supply chain in China and can also provide references for other countries with similar experiences as China in fresh agri-food production.Originality/value: This research introduces the option contract into fresh agri-food supply chain and takes information sharing and the risk caused by uncertain demand into consideration.

In a dynamic, uncertain environment, increased supply chain resilience can improve business quality. Predicting changes in enterprise supply chain resilience can help enterprises adjust their operational strategy timeously and reduce the risk of supply and demand interruption. First, a comprehensive resilience assessment framework for manufacturing enterprises was constructed from the perspective of the supply chain, and an improved technique for order of preference by similarity to the ideal solution (TOPSIS) method was used to quantify the resilience level. Considering that the resilience index is easily affected by uncertain factors, and this produces large fluctuations, the buffer operator and metabolism idea are introduced to improve the grey prediction model. This improvement can realize dynamic tracking of the enterprise resilience index and evaluate changes in the enterprise resilience level. Finally, through the analysis of the supply chain data of a famous electronic manufacturing enterprise in China over a two-and-a-half-year period, the results show that the improved TOPSIS method and the improved grey prediction model are effective in improving the supply chain resilience of manufacturing enterprises. This study provides a reference method for manufacturing enterprises to improve their supply chain resilience.

Improving the accuracy of financing risk prediction is of great significance to the healthy development of grid enterprises. Taking a provincial-level power grid company as the research object, the financing risk index system is constructed by considering multiple dimensions, and the monthly financing risk index RI of power grid enterprises from 2015-2018 is determined based on entropy weight and comprehensive index method, while the financing risk prediction model is constructed with the help of extreme gradient boosting tree model. The empirical results show that compared with support vector regression and BP neural network models, the financing risk prediction model constructed based on the extreme gradient boosting model has an excellent performance in terms of prediction accuracy and stability.

Purpose: Encouraging positive repeat purchasing behaviour among consumers is an effective pathway for numerous fresh agri-food platforms to achieve sustainable development. This study aims to explore the factors and underlying mechanisms that affect consumers’ loyalty in agri-food e-commerce, with a focus on the last-mile logistics service experience. Design/methodology/approach: The authors construct a theoretical research model with the last-mile logistics service experience (operationalized into three novel dimensions: functional, emotional and social) as the independent variable, customer satisfaction and trust as mediating variables and customer loyalty as the dependent variable. A regression analysis and empirical study were conducted on the data from 396 fresh agri-food e-commerce shoppers. Findings: The research results indicate that social experience can significantly influence customer loyalty directly (ß = 0.166, p < 0.001), while functional and emotional experiences exert indirect effects through mediators: emotional experience most strongly influences customer satisfaction (0.396) and functional experience most strongly influences customer trust (0.374). Specifically, emotional experience primarily drives loyalty via satisfaction, whereas functional experience primarily drives loyalty via trust. Originality/value: This study contributes to literature and practice by examining last-mile logistics service experiences in agri-food e-commerce. Uniquely, it dissects these experiences into functional, emotional and social dimensions, offering a nuanced framework. Practically, platforms can boost loyalty by prioritizing social experience, strengthening functional experience to build trust and refining emotional experience to improve satisfaction.