The green supply chain management (GSCM) is an enterprise’s effort to protect the environment and a key way to achieve sustainable environmental development. On the contrary, globalization brings more risks to the supply chain. Resilience has become a critical definition in supply chain management to help enterprises review the disruption and return to normal state. Therefore, choosing a resilient-green supplier to build a supply chain environment with flexibility and greenness under interruption becomes necessary for research works. However, the existing studies tended to focus on only one of the factors with resilience and greenness, and no comprehensive criteria system and performance value is expressed by a crisp number. Therefore, this paper proposes a hybrid method which integrates the Best-Worst method (BWM), Weighted Aggregated Sum-Product Assessment (WASPAS), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to solve the critical problems. Firstly, BWM is used to weigh the criteria; secondly, intuitionistic fuzzy numbers are introduced into the ranking stage. Then, the integrated WASPAS and TOPSIS are used to rank the alternatives to select the optimal resilient-green supplier. Finally, an illustrative example proves the feasibility of this method.

The development of the fresh-food e-commerce has led scholars to pay more attention to research on the agricultural product supply chain. This paper analyses the operation mode of the new O2O retail fresh supply chain by constructing the Stackelberg game model, takes the freshness and freshness-keeping efforts of fresh agricultural products as the main considerations, and analyses and compares the overall income of the supply chain under different decision-making modes. The results of the study show that: (1) In a centralized decision-making model, collaboration between suppliers and retailers online and offline can increase their respective revenues, and overall supply chain profits increase as the proportion of collaboration increases; (2) compared to decentralized decision-making, revenue-sharing contracts can increase the overall profitability of supply chain members and the supply chain when the revenue sharing factor is relatively high in the case of online and offline channel collaboration; (3) finally, the impact of revenue-sharing contracts on supply chain profitability is discussed through numerical analysis.

This study investigates the role of organizational information processing theory (OIPT) (Galbraith, 1974) in enhancing corporate and business decision-making within the complex and dynamic landscape of supply chain management (SCM). By conducting a comprehensive bibliometric analysis of peer-reviewed articles indexed in the Web of Science (WoS), utilizing bibliometric tools such as VOSviewer (van Eck & Waltman, 2010) and Scimago Graphica (Hassan-Montero et al., 2022), this research aims to map the intellectual structure and evolution of OIPT in the SCM domain. The study explores the current state of OIPT application in SCM, identifies key research themes and trends, and uncovers the relationships between OIPT and other relevant theoretical frameworks. Findings reveal that OIPT has emerged as a critical lens for understanding how organizations process information to make effective SCM decisions, particularly in emerging technologies like the Internet of Things (IoT), artificial intelligence (AI), and blockchain. This research contributes to the theoretical development of SCM by elucidating the mechanisms through which information processing influences supply chain performance. Furthermore, it provides practical implications for managers seeking to leverage information technology (IT) to improve decision-making and build resilient supply chains. By identifying knowledge gaps and promising research avenues, this study stimulates future research on integrating OIPT with other theoretical perspectives and exploring its application in specific SCM contexts.