The global halal market is growing, driven by rising stakeholder populations and increasing consumer interest in ethical and sustainable food choices. This surge in demand necessitates robust halal compliance throughout complex supply chains. However, there are several challenges, including fragmented information, increased understanding of halal requirements among stakeholders, and difficulties in tracing product provenance. This paper proposes a holistic framework for halal certification and compliance, addressing these challenges through the integration of artificial intelligence (AI) and blockchain technologies. AI can automate halal compliance checks, identify potential irregularities in sourcing and composition, and facilitate risk management. The blockchain offers an ideal platform for tracking product provenance throughout the halal supply chain. This ensures trust and confidence among consumers by providing verifiable information on ingredient origin and production processes. This paper further strengthens the potential of this framework by presenting an illustrative example that utilises knowledge graphs, machine learning, and smart contracts. This exemplifies the potential application of the proposed framework in the context of halal pre-certification processes. By fostering transparency and streamlining compliance procedures, the proposed holistic framework, empowered by AI and the blockchain, can significantly enhance trust and confidence among stakeholders within the halal food industry.

With the advent of "Industry 4.0" and the associated disruption caused by such rapid digital transformation, the logistics industry was able to develop cross-border delivery and increase demand for any given product. However, the risks associated with Cross-Border Reverse Logistics (CBRL) remain largely unexplored, and there is a paucity of research in this area. Hence, this study's objective is to investigate the ecommerce delivery system and CBRL model of a major fashion brand. The study is based on a case study approach comparing the CBRL processes of the Zara official site with one of their main channel partners, Alibaba. Data was collected through archival records, a secondary data bank and interviews with Zara representatives for a deeper analysis. The analysis reveals that both Zara and Alibaba's official sites currently have issues with their CBRL operations. The development of cross-border logistics represents a great opportunity to improve current Zara's and Alibaba's e-commerce platforms business model. In a very competitive market, a lack of a cross-border logistics system could represent a potential loss. Compared to local reverse logistics, cross-border remains at a relatively primitive stage. However, local reverse logistics can provide insights to Zara and Alibaba towards developing and improving CBRL. The case findings lay the foundation for a better understanding of CBRL e-commerce's challenges and support the need to optimize these systems to improve customer service in China.

This study investigates how dynamic capabilities—operational, collaboration, and learning capabilities—affect fresh agricultural supply chain performance (FASCP) in Vietnamese firms, focusing on the moderating role of supply chain uncertainty (FASCU). In this volatile, perishable sector, these capabilities are essential. Using a mixed-method approach, it combines quantitative analysis via PLS-SEM with qualitative insights to provide a comprehensive view. Results show operational and collaboration capabilities enhance FASCP, while learning capability has limited short-term effects. FASCU moderates these impacts, emphasizing the need for adaptive strategies. This study offers insights into optimizing fresh agri-supply chains in developing countries.

The primary focus of this study is to explore and answer the following research question: “In what ways can organizations enhance the efficiency and responsiveness of their reverse logistics network design?” The study presents a conceptual framework that the authors have built, incorporating several essential variables to guide the process of network design. The conceptual framework for reverse logistics networks has been developed by conducting an analysis of the fundamental factors that influence network design decisions. This paradigm facilitates the decision-making process for firms when considering the centralization or decentralization of their reverse logistics operations and the outsourcing or insourcing of specific operational aspects. Moreover, the authors have discovered and highlighted relevant literature that exhibits strong alignment with our proposed framework, hence offering useful insights for decision-making in the domain of reverse logistics network design. Within our framework, two crucial dimensions are considered at each stage: the choice between internal processing vs. outsourcing and the decision to centralize or decentralize operations. With consideration of the factors noted above, this paper proposes a mixed integer linear programming model for the design of a reverse logistics network. This model aims to minimize the total cost at each stage of reverse logistics operations.

The purpose of this study is to investigate the factors that motivate nanotechnology adoption in the food industry. The study collects 101 responses from food industry operators in Klang Valley, Malaysia, via a quantitative field survey. The data was analysed using Structural Equation Modelling (SEM). The findings show that effort expectation, price value, and trust have no statistical impact on nanotechnology adoption, whereas performance expectancy and top management support have positive effects. Given that this study is one of the few that investigates factors influencing nanotechnology adoption, it should give useful information for future studies on the food industry and nanotechnology adoption among researchers and practitioners.