Knowing the challenges of keeping and manipulating more and more immutable transaction records in a blockchain network of various supply chain parties and the opportunities of leveraging sophisticated analyses on the big data generated from these records, design of a robust blockchain architecture based on a cloud infrastructure is proposed. This paper presents this technical design with consideration of the technical sustainability in terms of scalability and big data processing and analytics. A case study was used to illustrate how the technical sustainability is achieved by applying the proposed technical design to the real-time detection of the maritime risk management. This case also illustrates how machine learning mechanism helps to reduce maritime risk by guiding a cargo ship to adjust to the planned or safe route from a detour to a danger zone. This paper also discusses the implications for further research direction.

Inspired by the discontinuation of the blockchain platform TradeLens, co-developed by IBM and Maersk, due to the lack of the involved supply chain stakeholders’ adoption, a critical literature review on the models of supply chain stakeholders’ adoption of blockchain applications was conducted. This review is significant as it provides insights into the exploration of a more universal approach to investigate which factors really influence blockchain adoption, which is a pre-requisite for the technical sustainability of blockchain technology in supply chains. As observed in the review, the technology acceptance model (TAM), the technology–organization–environment (TOE) framework, and the unified theory of acceptance and use of technology (UTAUT) are frequently used in the literature, but little attention has been paid to whether blockchain technology fits the users’ tasks in understanding blockchain adoption in the supply chain. Among the technology adoption theories, task–technology fit (TTF) considers whether a technology fits the tasks, but only two previous studies involved the use of TTF. This study discusses the suitability of these existing models of technology adoption for blockchain applications in supply chains and comes up with a new unified model, namely TOE-TTF-UTAUT. This review also has implications for a more appropriate conceptual research design using mixed methods.

In the last six years, there has been a rise in research interest with regard to the applications of blockchain technology in supply chains and how these applications bring benefits to supply chain management. In a broader sense, an essential research focus that has been discussed in the literature is the way in which this emerging blockchain technology in supply chains brings sustainable benefits to a community. The rationale for incorporating cloud technology into a blockchain and integrating the blockchain with machine learning for supply chain applications is to maintain technical sustainability. While previous studies suggested and reported sustainable practices of applying blockchain technology in supply chains, the means with which these practices are brought about by the cloud-based blockchain integrated with machine learning (CBML) have not been thoroughly explored in the literature. The case study presented in this paper aims to fill this gap by exploring technically, environmentally, economically, and socially sustainable practices through the use cases of CBML for supply chain management by the international leading container shipping company Maersk. The use cases by Maersk presented in published documents were collected from the Internet and then analyzed. This document analysis was performed in two ways. The first way was a technical review of the blockchain technology used by Maersk with a consideration of technical sustainability to ensure scalability and big data analytics. The other way was to analyze the applications of the CBML by Maersk to indicate how environmental sustainability, economic sustainability, and social sustainability can be achieved. On the other hand, this paper also highlights the negative technical, environmental, economic, and social sustainability impacts caused by Maersk and discusses implications for future research directions.

Generic competence (GC) development is an integral part of higher education to provide holistic education and enhance student career development. It also plays a critical role in complementing the curriculum. Many tertiary institutions provide various GC development activities (GCDA). Moreover, institutions strongly need to further understand student partici- pation, especially its relationship to student backgrounds, activity profiles, and academic results. With the fast advancement of educational technologies and data mining, data analytics (DA) in formal learning and online education has been widely explored. However, there has been little work on student behavior in GCDA. To fill this gap and to provide new contributions, we conduct a comprehensive study to investigate the interrelation- ship of GCDA participation and academic performance before and after higher education with significant and representative data (over 10,000 records) across three years. Hypotheses are formulated and validated, and the findings are triangulated with machine learning and DA. With supervised learning, the predictors of academic performance and GCDA participation are formulated, and the features to enhance predictions are analyzed. We develop predictors using novel approaches of genetic algorithm and Stacking in machine learning. The impacts of the breadth and depth of involvement is also studied. Results indicate that involvement in GCDA positively impacts student academic results. Our novel approaches give improvements in predicting student participation. Our holistic studies covering hypothesis validation, data analysis and machine learning provide valuable insights into GCDA development.

The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students’ particulars such as their academic background, current study and student activity records, their attended higher education institution’s expectations of graduate attributes and self-assessment of their own generic competencies. The gap between the higher education students’ generic competence development and their current statuses such as their academic performance and their student activity involvement was incorporated into the framework to come up with a recommendation for the student activities that lead to their generic competence development. For the formulation of the recommendation framework, the data mining tool Orange with some programming in Python and machine learning models was applied on 14,556 students’ activity and academic records in the case higher education institution to find out three major types of patterns between the students’ participation of the student activities and (1) their academic performance change, (2) their programmes of studies, and (3) their English results in the public examination. These findings are also discussed in this paper.

Inspired by the challenges of keeping secure and private lifelong longitudinal health-related transactions in a distributed and shared, encrypted and access controlled, immutable and rapidly growing blockchain for public health, a cloud infrastructure with machine learning (ML), data analytics, security and privacy considerations has to be designed. This chapter presents how this proposed cloud infrastructure facilitates distribution and sharing of health transactions and discusses how the security and privacy mechanisms work for the public health stakeholders to access and analyse anonymous health data stored in the blockchain in the cloud platform. Also, the immutability and rapid volume growth features of the historical transactional data stored on the public health blockchain provide big data for the health care practitioners, researchers and government to analyse and get insights from. In this regard, those transactional data relationships have to be managed effectively for efficient data processing and data analytics integrated with learning machine models can be adopted to examine the data for sophisticated analyses for health care concerns. This chapter exhibits how ML can be integrated into data analytics and highlights some applications on cloud using Amazon Web Services as an example.