Whilst there are promising links between the Internet of Things (IoTs), dynamic data and information processing capabilities (DDIPCs), and operational agility, scholars have not conducted enough empirical studies that offer convincing evidence for the use of the IoTs and relevant linkages. This study therefore examines the links between such constructs and provides managerial implications for contemporary data and information driven managers who adopt evidence-based decision making for better operational outcomes. The results obtained from structural equation modelling indicate that the use of the IoTs is the key determinant for operational agility and also plays a vital role in establishing DDIPCs that further reinforce it. Additionally, DDIPCs mediate the relationship between the use of the IoTs and operational agility. By persuasively building these links based on theoretical arguments and testing them by using a unique dataset, this study contributes to the deeper understanding of the mechanisms by which the use of the IoTs and DDIPCs strengthen operational agility.

In the last decade, food and drink supply chain management has become an important part of global operations strategy. The global food and drink industries (FDIs) is establishing supply chain operations across countries as a result of increasing demand, this expansion has created challenges in coordinating operations that connect multi-suppliers, one as such is the financial enabler for the multi-layered supply chain network. However, literature on artificial intelligence (AI) in FDIs is limited, this study explores AI theory in supply chain networks and alternative supply chain financing for the FDIs. This study proposes a new conceptual framework based on theoretical contributions identified through literature, a conceptual framework is established and further developed to a meta-framework. This study explored the set-theoretic comparative approach for data analysis, the outcomes of this research suggest that the probable contributions of supply chain networks driven by AI technologies provide a sustainable financing stream for the food and drink supply chain. KEYWORDSArtificial intelligence food and drink industries supply chain finance sustainability supply networksThe food and drink industries (FDIs) have been facing immense cash flow challenges that are affecting operations; as a result, firms are finding difficulties in sourcing funds to meet customer and supplier demands (Yakovleva, Sarkis, and Sloan 2012). In this environment, supply chain finance has become the focal point of business financing, especially since the last recession where financial services support for global supply chain industries and operations has been reduced or withdrawn (Lekkakos and Serrano 2016). Therefore, we explore the important impact of Artificial Intelligence (AI) in stimulating financial services for FDIs through supply chain network activities.One of the impact of the economic collapse is shortage of liquidity for the FDIs (Huang, Yang, and Tu 2019). During this challenging periods, FDIs initiated the trade credit system as an alternative form of financing enabling suppliers to continue doing business, consequently leading to eventually worse situation in the supply chain (upstream) (Huang, Fan, and Wang 2019). The consequences of this financial crisis contributed to the impulse for innovative solutions that support and optimise cash flow. Among these solutions, supply chain finance (SCF) is one of the significant strategies, with the aim to ensure sustainable financial flows within the industry by implementing technologically advanced solutions such as AI.Although there is a consensus on the impact of the financial crisis in supply chain (SC) leading to the initiative of supply chain finance. Thus, literature identify two views on the SCF: the first view is referred to as the 'supply chain-oriented' SCF, encirclements operational financial capital decisions described in its components such as cash flow and accounts payables. In addition, this perspective focuses on the optimisation of operational financial flows for FDIs...

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Supply Chain Finance (SCF) is receiving increasing awareness in research as a result of uncertainties in the global financing for supply chain (SC). There are limited and fragmented studies in the implementations of financial services in supply chain management. This study builds on recovery from the financial crisis of 2008 and posts COVID-19 pandemic, where uncertainties crippled SCF providers and brokers services. At the same time, cutting-edge technological advancements such as artificial intelligence (AI) are revolutionizing the processes of business ecosystem in which SCF is entrenched. This study thus adopts a fuzzy set theoretical approach to unpack the entities relationship validity for sustainable SCF mate-framework, and the originality of AI concepts to sustainable SCF to identify the issues and inefficiencies. The results indicate that AI contributes significant economic opportunities and deliver the most effective utilization of the supply networks. In addition, the study provides a theoretical contribution to financing in SC and broadens the managerial implications in improving performance.

Knowledge management has been identified as a key enabler to achieve organisation's value chain competitiveness. It, however, has been facing fresh challenges in a global supply chain setting. This paper proposes a global knowledge chain management (GKCM) framework that identifies and prioritises critical knowledge that a global supply chain can focus on to support integrated decisions. The framework explores three types of global context knowledge, namely global market knowledge, global capacity knowledge and global supply network configuration knowledge. Empirical study has been undertaken within the manufacturing industry to evaluate the GKCM framework. Analytic network process has been explored as a key method to assess the importance of the global knowledge constructs from supply chain managers' perspectives. A key contribution of the paper is that it advances existing knowledge chain management approaches within one organisation and its local supply chain to include the global context knowledge applicable to global manufacturing settings, and highlights how the GKCM framework can support global supply chain integrated decisions.