Purpose: ChatGPT is a cutting-edge chatbot powered by artificial intelligence that could revolutionise and advance the teaching and learning process. Drawing on the technology acceptance model (TAM) and information system (IS) success model, this study aims to investigate determinants of students’ intention to use ChatGPT for education purposes. Design/methodology/approach: The partial least squares technique was used to analyse 406 usable data collected from university students in Malaysia. Findings: The results confirmed the relationships between perceived usefulness (PU), perceived ease of use (PEU), attitude and intention to use proposed by TAM. PU and PEU are influenced by system quality. Surprisingly, trust in information moderates negatively the influences of PEU and PU on attitude. Practical implications: The findings provide insight for higher education institutions, unit instructors and ChatGPT developers on what may promote the use of ChatGPT in higher education. Originality/value: The study contributes to the literature by exploring the determinants of ChatGPT adoption, extending the TAM model by incorporating IS success factors and assessing the moderating effect of trust in information.

The study investigates the determinants of trust in sellers and products and purchase intention in the social commerce (s-commerce) context by considering the moderating effects of trust disposition and perceived price fairness. The data were collected from 416 individuals who have followed at least one seller on Instagram and analysed using the Partial Least Squares (PLS) approach. The findings revealed that review quantity, review quality, perceived symmetric product information, and responsiveness positively influence trust in seller. The direct influence of review quality on trust in products was confirmed. Trust disposition negatively moderates the impacts of review quality on trust in sellers and responsiveness on trust in products. Furthermore, perceived price fairness positively moderates the influence of trust in sellers and products on purchase intention. The findings extend the literature on s-commerce in several ways. The findings enable s-commerce sellers to formulate effective marketing strategies and boost purchase intention.

Technological advances such as blockchain, artificial intelligence, big data, social media, and geographic information systems represent a building block of the digital transformation that supports the resilience of the food supply chain (FSC) and increases its efficiency. This paper reviews the literature surrounding digitalization in FSCs. A bibliometric and key-route main path analysis was carried out to objectively and analytically uncover the knowledge development in digitalization within the context of sustainable FSCs. The research began with the selection of 2140 articles published over nearly five decades. Then, the articles were examined according to several bibliometric metrics such as year of publication, countries, institutions, sources, authors, and keywords frequency. A keyword co-occurrence network was generated to cluster the relevant literature. Findings of the review and bibliometric analysis indicate that research at the intersection of technology and the FSC has gained substantial interest from scholars. On the basis of keyword co-occurrence network, the literature is focused on the role of information communication technology for agriculture and food security, food waste and circular economy, and the merge of the Internet of Things and blockchain in the FSC. The analysis of the key-route main path uncovers three critical periods marking the development of technology-enabled FSCs. The study offers scholars a better understanding of digitalization within the agri-food industry and the current knowledge gaps for future research. Practitioners may find the review useful to remain ahead of the latest discussions of technologyenabled FSCs. To the authors’ best knowledge, the current study is one of the few endeavors to explore technology-enabled FSCs using a comprehensive sample of journal articles published during the past five decades.