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last recession where financial services support for global supply chain industries and operations has been reduced or withdrawn (Lekkakos and Serrano 2016).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.

Fake news (FN) on social media (SM) rose to prominence in 2016 during the United States of America presidential election, leading people to question science, true news (TN), and societal norms. FN is increasingly affecting societal values, changing opinions on critical issues and topics as well as redefining facts, truths, and beliefs. To understand the degree to which FN has changed society and the meaning of FN, this study proposes a novel conceptual framework derived from the literature on FN, SM, and societal acceptance theory. The conceptual framework is developed into a meta-framework that analyzes survey data from 356 respondents. This study explored fuzzy set-theoretic comparative analysis; the outcomes of this research suggest that societies are split on differentiating TN from FN. The results also show splits in societal values. Overall, this study provides a new perspective on how FN on SM is disintegrating societies and replacing TN with FN.

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