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The Fourth Industrial Revolution (4IR) and its associated technologies are revolutionising socio-economic sectors, including agriculture. This article explores the impact of digitalisation on African food systems. The article uses secondary and primary sources in academic and grey formats to examine how digital tools can shape agriculture and create new opportunities for developing Africa's food system. It identifies digital tools usable across the food system chain and explores their potential impact on food production, processing, storage, marketing, transportation, and consumption. The article also examines the challenges to transforming the digital food system in Africa. The article shows that while digital technology has the potential to increase food production, reduce food waste, and improve food marketing and distribution, several economic, political, and social challenges exist that frustrate digital uptake. It argues that African governments should enhance the capacity of actors in the food systems chain to adopt digital tools. This can be achieved through adopting relevant policies and rolling out digital infrastructure to enable actors to access it in their areas of operation.

The advancement of artificial intelligence (AI) and its growing societal importance are reshaping decision-making processes and policy analysis roles. This study examines the readiness of manufacturing companies in Malaysia to embrace AI technology, considering its potential to enhance decision-making, productivity, quality control, job automation, and data analysis. Focusing on the Technology, Organization, and Environment (T-O-E) readiness framework, the research investigates the relationship between these dimensions and AI adoption readiness among manufacturing companies in Shah Alam, Selangor, Malaysia. AI adoption readiness serves as the dependent variable, while technological, organizational, and environmental readiness dimensions act as independent variables. The study applies the T-O-E framework to AI readiness and proposes a framework for assessing AI readiness at the manufacturing level. It identifies factors influencing readiness within the technological, organizational, and environmental dimensions, including relative advantage, compatibility, resources, competitive pressure, top management support, and government regulations. Through rigorous analysis, patterns, trends, and correlations are revealed, highlighting a significant link between the T-O-E readiness dimensions and AI adoption readiness. Notably, organizational readiness emerges as a key driver of AI adoption in Malaysian manufacturing companies. The results of this investigation have broad implications, offering suggestions to improve organizational preparedness and unlock AI’s potential benefits for businesses in the industrial sector. Additionally, the research lays the groundwork for further studies on AI readiness across various industries and international contexts. As AI becomes increasingly integrated into manufacturing processes, adaptive businesses gain competitive advantages on a global scale. These advantages include increased productivity, informed decision-making, streamlined quality control, improved customer satisfaction, and potential contributions to economic growth. The study concludes by recommending strategies to reinforce organizational readiness and emphasizes the need for future research to deepen understanding of AI adoption readiness in the manufacturing industry. The integration of AI technology offers benefits such as enhanced productivity, decision-making, quality control, and customer satisfaction, granting businesses a competitive edge in the digital landscape and increasing stakeholder interest.

Eco-friendly product usage is a defining trend in consumer purchase intentions during this decade. Younger, tech-savvy consumers are often more sensitive to environmental concerns when purchasing products due to the abundance of information available on social media platforms. In the food and beverage industry, this trend has led many younger consumers to prefer purchasing eco-friendly, sustainable cutlery for food and beverage consumption. With online social commerce platforms selling eco-friendly utensils readily available, a larger group of consumers, primarily young adults, seem to purchase these products impulsively due to various factors. The four independent variables proposed in this study that seem to influence the online impulse buying behavior of young adults toward these eco-friendly products are fear, serendipity, electronic word of mouth (eWOM), and website quality. Convenience sampling was utilized to gain a greater understanding of the targeted consumer group, using a 5-item Likert scale to collect data from respondents. The validity and reliability of the questionnaire items were further confirmed using statistical methods, namely average variance extracted (AVE), composite reliability (CR), and Cronbach’s alpha. The hypotheses were tested using AMOS software (version 24) through confirmatory factor analysis (CFA) to tabulate the results. Based on the study conducted, it is concluded that fear and eWOM do influence the online impulse buying of eco-friendly food utensils among young adults in Kuala Lumpur. It is further suggested that elements such as fear and eWOM play a significant role in online impulse purchases of eco-friendly utensils, as young adults rely heavily on social media marketing. This reliance contributes to these consumers’ growing concern for their future, prompting them to be more environmentally conscious.

Online review sites and social media platforms have become crucial sources of information for consumers, greatly influencing their purchasing behavior and decision-making, especially in the food and beverage industry. However, not many studies have been carried out to understand how social media influences customers’ purchase intentions at fast-food restaurants. This paper examines the growing influence of social media attributes on purchase intentions, with a focus on the Malaysian context. Previous studies highlight the increasing importance of social media platforms as marketing tools and their impact on customers’ purchase intentions. The research explores the relationship between habit and informativeness and their influence on purchase intentions among young adults in fast-food restaurants. A purposive sampling method was utilized to gather data from 142 fast-food customers through a cross-sectional online survey. The research hypotheses were analyzed using the partial least square structural equation modeling. Key findings found that habit and informativeness have a significant positive impact on customers’ intentions to purchase at fast-food restaurants. The study contributes to the existing literature by highlighting the growing influence of social media on consumer behavior in the context of fast-food restaurants. It adds to our understanding of how social media platforms serve as effective marketing tools, particularly among young adults, and how they can influence purchase intentions. By understanding the interplay of habit, informativeness, and purchase intentions, businesses can develop more effective marketing strategies and foster stronger relationships with their customers.

Portfolio optimization has always been the main concern of investors. What differentiates different optimization models from each other is the risk measure. The main contribution of this paper is to provide a portfolio optimization model that considers systemic risk so that it can help investors make optimal investment decisions as a general model. For this purpose, two models are presented. In the first model, systemic and systematic risk were considered simultaneously, and in the second model, only systemic risk was considered. In the two mentioned models, delta conditional value at risk (∆CoVaR) and the Markowitz model are used respectively to measure systemic risk and a benchmark model. Also, the criteria used to compare the performance of the reviewed models include the ratio of reward-to-risk, along with the Sortino ratio and the Omega ratio. The problem of optimization and examination of the results was carried out on a selected sample, 38 companies listed in the Tehran Stock Exchange (TSE) from 2013 to 2023. The results of empirical analysis of out-of-sample data (during a period of 1198 days) show that based on all three mentioned criteria, the first proposed model shows the best performance among the three models. In addition, the performance of the second model is ranked second. In short, it can be said that considering systemic risk in portfolio optimization leads to better performance than the Markowitz model.