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This case study aims to examine the discourses of Early Children Education (ECE) curriculum and preservice teachers’ teaching practicum in Hong Kong to explore issues of developing preservice teachers as leaders for their future career. Adopting the qualitative case study methodology, semistructured interviews and documentation were mainly used for data collection to address the following research questions: (a) To what extent are preservice teachers in ECE in Hong Kong aware of the needs of leadership development for their future career? (b) To what extent are the preservice teachers in ECE in Hong Kong able to be developed as leaders in the process of teacher education? (c) What are factors influencing the leadership development of preservice teachers in preschools in Hong Kong? Documents such as program handbooks, field experience handbooks, and student participants’ teaching portfolios were collected for analysis. Both teacher educators and preservice teachers were invited for individual interviews to reflect on their experiences of supervising or participating in teaching practicum. The findings revealed that both teacher educators and preservice teachers were aware of the importance of developing preservice teachers as leaders. The teaching practicum provided various opportunities for preservice teachers to develop leadership skills. However, personality and learning experiences provided in the curriculum will also impact on leadership development. This study also informs policymakers, curriculum developers, and teacher educators about possible curriculum changes and potentials of developing preservice teachers as leaders for their future career.

This chapter restates the purpose of the three-volume series and discusses themes that reoccur in chapters and sections of Part B, which first appeared in chapters and sections of Part A of the series. While Part A of the three-book set focused on pedagogies of teacher selection, reflection, narrative ways of knowing, identity, and mentoring and mediation, Part B of the three-volume series centers on pedagogies of preservice teacher leadership, diversity, parents and family, social justice, and technology. Ideas having to do with traveling stories, the theory-practice split, and the praxical nature of pedagogies are taken up. To conclude, the model for traveling pedagogies, which was first proposed in Part A of the series, once again appears, with a few sub-themes added from International Teacher Education (Part B), which support the already identified framework in International Teacher Education (Part A).

Introduction: The world is passing through a technology explosion phase where one technology is being replaced by another very quickly. Emerging technologies play more important roles in the insurance sector directly or indirectly. These technologies have a high potential to change the insurance paradigm.

Purpose: In this chapter, we discuss emerging technologies such as artificial intelligence (AI), big data, blockchain, the internet of things (IoT), mobile technology, predictive analytics, social media, telematics, chatbots, low codes, and drones in the context of the insurance industry.

Methodology: To carry out our analysis, we searched for data using the keywords for each technology from the Web of Science (WoS) coral database. Certain inclusion and exclusion criteria were followed to select the articles for further analysis. R-studio was used for the data analysis and visualisation.

Findings: It was found that the highest number of research articles published are related to big data, followed by AI and social media. The first article on AI in insurance appeared in 1975. Social media is the highest cited new technology, whereas the low codes are the undiscovered paradigm for the insurance sector with no published research. Research on the impact of chatbots, drones, and mobile technology in the insurance industry is still at a nascent stage. We also noticed that the United States is leading the research on emerging technologies in the insurance sector.

Implications: This chapter audits the emerging technologies in the insurance sector and identifies technological areas with the highest, least, or no research, dominant journals, authors, and countries. This holistic overview empowers managers and academicians to decide the future course of action.

The sustainable transition towards the circular economy requires the effective use of artificial intelligence (AI) and information technology (IT) techniques. As the sustainability targets for 2030–2050 increasingly become a tougher challenge, society, company managers and policymakers require more support from AI and IT in general. How can the AI-based and IT-based smart decision-support tools help implementation of circular economy principles from micro to macro scales?

This chapter provides a conceptual framework about the current status and future development of smart decision-support tools for facilitating the circular transition of smart industry, focussing on the implementation of the industrial symbiosis (IS) practice. IS, which is aimed at replacing production inputs of one company with wastes generated by a different company, is considered as a promising strategy towards closing the material, energy and waste loops. Based on the principles of a circular economy, the utility of such practices to close resource loops is analyzed from a functional and operational perspective. For each life cycle phase of IS businesses – e.g., opportunity identification for symbiotic business, assessment of the symbiotic business and sustainable operations of the business – the role played by decision-support tools is described and embedding smartness in these tools is discussed.

Based on the review of available tools and theoretical contributions in the field of IS, the characteristics, functionalities and utilities of smart decision-support tools are discussed within a circular economy transition framework. Tools based on recommender algorithms, machine learning techniques, multi-agent systems and life cycle analysis are critically assessed. Potential improvements are suggested for the resilience and sustainability of a smart circular transition.