The Internet of things (IoT) is a next generation of Internet connected embedded ICT systems in a digital environment to seamlessly integrate supply chain and logistics processes. Integrating emerging IoT into the current ICT systems can be unique because of its intelligence, autonomous and pervasive applications. However, research on the IoT adoption in supply chain domain is scarce and acceptance of the IoT into the retail services in specific has been overly rhetoric. This study is drawn upon the organisational capability theory for developing an empirical model considering the effect of IoT capabilities on multiple dimensions of supply chain process integration, and in turn improves supply chain performance as well as organisational performance. Cross-sectional survey data from 227 Australian retail firms was analysed using structural equation modelling (SEM). The results indicate that IoT capability has a positive and significant effect on internal, customer-, and supplier-related process integration that in turn positively affects supply chain performance and organisational performance. Theoretically, the study contributes to a body of knowledge that integrates information systems research into supply chain integration by establishing an empirical evidence of how IoT-enabled process integration can enhance the performance at both supply chain and organisational level. Practically, the results inform the managers of the likely investment on IoT that can lead to chain’s performance outcome.

Purpose: Theorising from the intersection of supply chain and information systems (IS) literature, this study aims to investigate supply chain integration (SCI) as a multidimensional construct in the context of cloud-based technology and explores the effect of cloud-enabled SCI on supply chain performance, which will eventually improve firm sustainability from a resource-based view (RBV). In addition, the moderating effect of top management is explored.Design/methodology/approach: Using cross-sectional survey data collected from a sample of 105 Australian retail firms, this study used structural equation modelling to test the hypothesised relationship of cloud-enabled SCI with performance in a theoretical model.Findings: Results show that cloud-based technology has positive effect on SCI, and the cloud-enabled SCI is positively related to supply chain performance which eventually influenced firm sustainability. Further, top management intervention moderates the relationship between supplier and internal integration with supply chain performance. But it is found to have no moderating effect on the relationship between customer integration and supply chain performance.Practical implications: Recognising the potential benefits of emerging cloud-based technologies reported in this study, retail managers need to understand that higher order SCI requires the support of cloud-based technology to improve supply chain performance and firm sustainability.Originality/value: This research extends prior research of information and communication technologies-enabled SCI and its effect on supply chain performance which overly remains inconsistent. In addition, IS literature abounds with discussion on cloud computing technologyper se, and its adoption in supply chain is overly rhetoric. This study fills this gap by conceptualising the multiple dimensions of SCI enabled by cloud-based technology and the way it affects supply chain and firm sustainable performance. Investigating SCI in context of cloud-based technology is a unique contribution in this study. The moderating effect of top management in this decision also adds to the current body of literature.

AI (Artificial intelligence) has the potential to improve strategies to talent management by implementing advanced automated systems for workforce management. AI can make this improvement a reality. The objective of this study is to discover the new requirements for generating a new AI-oriented artefact so that the issues pertaining to talent management are effectively addressed. The design artefact is an intelligent Human Resource Management (HRM) automation solution for talent career management primarily based on a talent intelligent module. Improving connections between professional assessment and planning features is the key goal of this initiative. Utilising a design science methodology we investigate the use of organised machine learning approaches. This technique is the key component of a complete AI solution framework that would be further informed through a suggested moderation of technology-organisation-environment (TOE) theory with the theory of diffusion of innovation (DOI). This framework was devised in order solve AI-related problems. Aside from the automated components available in talent management solutions, this study will make recommendations for practical approaches researchers may follow to fulfil a company’s specific requirements for talent growth.