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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.

Investments in gender training for agricultural researchers have not attained expected outcomes, bringing into question the efficacy of training approaches used. New approaches for transformative gender training need to draw on lessons learned from previous courses. This chapter analyses short gender training courses identified using a scoping methodology. Selected courses offered between 2005 and 2015 for scientists in Eastern Africa were critiqued against a theoretical framework for transformative gender training. Also shared is a training model (Gender Responsive Researchers Equipped for Agricultural Transformation course) that addresses gaps in previous courses. The chapter identifies critical lessons for facilitating transformative gender training for non-gender research practitioners which include the need for inter-disciplinary grounding in the disciplines of gender and agriculture, having a phased course delivery and the value of continuous technical support during and after training. Gender training models should also allow for the deepening of gender awareness and consciousness by providing safe spaces for personal reflections on the root causes of gender inequalities and for the questioning of the internalized norms and biases.

Meta-organizations are crucial devices to tackle grand challenges. Yet, by bringing together different organizations, with potentially diverging views on these grand challenges, meta-organizations need to cope with the emergence of contradictory underlying social orders. Do contradictory orders affect meta-organizations’ ability to govern grand challenges and if so, how? This paper investigates these essential questions by focusing on the evolution and intermeshing of social orders within international governance meta-organizations. Focusing on the International Whaling Commission and the grand challenge of whale conservation, we show how over time incompatible social orders between the meta-organization and its members emerge, evolve and clash. As our study shows, this clash of social orders ultimately removes the “decidability” of certain social orders at the meta-organizational level. We define decidability as the possibility for actors to reach collective decisions about changing an existing social order that falls under a collective’s mandate. We argue that maintaining decidability is a key condition for grand challenges’ governance success while the emergence of “non-decidability” of controversial social orders can lead to substantial failure. We contribute to both the emerging literature on grand challenges and organization theory.

The visualization of ranking information in global public policy is moving away from traditional “league table” formats and toward dashboards and interactive data displays. This paper explores the rhetoric underpinning the visualization of ranking information in such interactive formats, the purpose of which is to encourage country participation in reporting on the Sustainable Development Goals. The paper unpacks the strategies that the visualization experts adopt in the measurement of global poverty and wellbeing, focusing on a variety of interactive ranking visualizations produced by the OECD, the World Bank, the Gates Foundation and the ‘Our World in Data’ group at the University of Oxford. Building on visual and discourse analysis, the study details how the politically and ethically sensitive nature of global public policy, coupled with the pressures for “decolonizing” development, influence how rankings are visualized. The study makes two contributions to the literature on rankings. First, it details the move away from league table formats toward multivocal interactive layouts that seek to mitigate the competitive and potentially dysfunctional pressures of the display of “winners and losers.” Second, it theorizes ranking visualizations in global public policy as “alignment devices” that entice country buy-in and seek to align actors around common global agendas.

Although we have achieved a greater understanding of cryptomarket specifics, evidence on the consumer side of cryptomarkets is still needed – not only regarding the role of cryptomarkets on individual drug-using careers but also on the motives for buying illicit drugs from cryptomarkets. Moreover, research has indicated that national differences exist regarding different variables relating to cryptomarket use and prevalence, as well as to why users are drawn to these markets. In this chapter, the author presents the results of a Belgian case study focusing on drug cryptomarket buyers. Using an online quantitative survey (N = 99) and semi-structured interviews (N = 10), we gain exploratory insight into the motives of Belgian buyers sourcing illicit drugs from cryptomarkets and how they believe these cryptomarkets affect their drug-using careers. Results indicate that most of the respondents had bought drugs offline before buying them from cryptomarkets and that the frequency of their drug use did not change once cryptomarkets were accessed. Almost 60% of our respondents, however, consumed different drugs or a wider range of drugs following their cryptomarket use. Furthermore, most of the respondents purchased from cryptomarkets for their personal consumption, and some of them also shared their supply with friends, that is, social supply. The alternative drug offer was the principal reason why they were using cryptomarkets, followed by curiosity and the price and the quality of the drugs. Although the respondents in this study were well aware of different risks related to market vendors, market administrators, and law enforcement, these risks were considered to be minimal and part of the cryptomarket environment. The results of this case study are informative and highlight areas requiring further research.

Research to find new energy source is still an intensive work by researchers in this field. One of the energy sources with no negative impact to environment is solar energy. Solar cell is used to convert solar energy to electrical energy. The electrically powered solar cell in direct current (DC) power is not suitable for our daily office equipment since they need the alternating current (AC) power. This research has succeeded in realizing a solar cell automation tool based on Arduino Uno with input from solar energy, from which output AC voltage can be used for the needs of household appliances and office equipments. Output power of this tool is approximately 700 W, which can turn on the lights, charge the hand phones, laptops, and so forth.

The Models of Child Health Appraised (MOCHA) project identified the different models of primary care that exist for children, examined the particular attributes that might be different from those directed at adults and considered how these models might be appraised. The project took the multiple and interrelated dimensions of primary care and simplified them into a conceptual framework for appraisal. A general description of the models in existence in all 30 countries of the EU and EEA countries, focusing on lead practitioner, financial and regulatory and service provision classifications, was created. We then used the WHO ‘building blocks’ for high-performing health systems as a starting point for identifying a good system for children. The building blocks encompass safe and good quality services from an educated and empowered workforce, providing good data systems, access to all necessary medical products, prevention and treatments, and a service that is adequately financed and well led. An extensive search of the literature failed to identify a suitable appraisal framework for MOCHA, because none of the frameworks focused on child primary care in its own right. This led the research team to devise an alternative conceptualisation, at the heart of which is the core theme of child centricity and ecology, and the need to focus on delivery to the child through the life course. The MOCHA model also focuses on the primary care team and the societal and environmental context of the primary care system.

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.

In this chapter, we discuss talent development in the context of higher education. After discussing the advantages and disadvantages of inclusive and exclusive approaches to talent development, we present empirical data that detail how the participants of a focus group study perceive talent development in higher education. Our data show the importance of a contextualized reading of talent development as the competitive context in academia hinders an inclusive focus on talent development. This context results in a performance-centred, instead of a development-centred approach to talent management, where outperforming others in narrowly defined areas (e.g. publication record) is the main goal. We show that in such a context the development of competitive talent is rewarded, and the development of communal talent is not. The focus on performance instead of (inclusive) development becomes more pronounced when employees move through their career and is believed to have several negative consequences. Mostly women perceived that such a non-inclusive approach to talent development hinders the development and deployment of their talents and obstructs their career progression.