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We conduct a literature review on forms of organizing that address grand challenges, which are operationalized as the Sustainable Development Goals of the United Nations, as this framework is universal and widely adopted. By analyzing the articles that match our criteria, we identify six differentiable organizational forms: movements, temporary organizations, partnerships, established organizations, multi-stakeholder networks, and supranational organizations. These six forms are differentiated based on the two following categories: organizing segment and communicational technological approach. Our analysis shows that tackling a grand challenge often starts with collectives as a protest culture without any expected goal, besides sending an impulse to others. This impulse is received by criticized institutionalized organizations that have the capacity and resources to address the problem properly. However, new challenges arise as these organizations inadequately resolve these problems, thereby leading to conflict-laden areas of tension, wherein emergent organizations complement institutionalized organizations that have created the first infrastructure. To solve the most complex problems, a trichotomous relationship between different forms of organizations is necessary. Moreover, communicational technological approaches become more sophisticated as grand challenges increase in complexity.

This chapter is based on the findings of the empirical material gathered in Finland and Sweden through interviews with education and audiovisual (AV) media actors and policymakers in 2017–2018. The aim of the chapter is to discuss the innovation systems of the education sector and Finland and Sweden in general, compare the sectoral innovation models of the two sectors, and conclude with discussing the resulting challenges for policymakers. Our results show that a new EdTech sector employing the competences of the education, information and communication technology, and AV media sectors has begun to emerge and actors in the both countries have eagerly taken actions to boost its development as a business and export field. We discuss the reasons and consequences of this development.

Organizations across industries are increasingly using Artificial Intelligence (AI) systems to support their innovation processes, supply chains, marketing and sales and other business functions. Implementing AI, firms report efficiency gains from automation and enhanced decision-making thanks to more relevant, accurate and timely predictions. By exposing the benefits of digitizing everything, COVID-19 has only accelerated these processes. Recognizing the growing importance of AI and its pervasive impact, this chapter defines the “social value of AI” as the combined value derived from AI adoption by multiple stakeholders of an organization. To this end, we discuss the benefits and costs of AI for a business-to-business (B2B) firm and its internal, external and societal stakeholders. Being mindful of legal and ethical concerns, we expect the social value of AI to increase over time as the barriers for adoption go down, technology costs decrease, and more stakeholders capture the value from AI. We identify the contributions to the social value of AI, by highlighting the benefits of AI for different actors in the organization, business consumers, supply chain partners and society at large. This chapter also offers future research opportunities, as well as practical implications of the AI adoption by a variety of stakeholders.

Targets set out by state institutions, with respect to supplying water to deprived communities, seem to be idealistic and not realistic. Study envisioned to assess challenges of financing water infrastructure projects, and determines the role of the state towards infrastructure development by holistically planning and engaging with the private sector.

The study adopted a quantitative approach, whereby a questionnaire survey was conducted among different stakeholders involved in water infrastructure projects in South Africa. Data gathered were analysed using percentages, mean item score and standard deviation.

The study revealed that most challenges affecting the success of the financing of water infrastructure projects in South Africa are corruption, hostility towards private participation, cost recovery constraints, high fiscal deficits by state government, unreliable planning and procurement processes, and a rapid increasing number of municipalities that lack technical and administrative capacity to plan implement, operate and maintain water assets.

This research paper investigates projects’ financing challenges with a broad inspection on the role of the public sector. The apparent role of the international structures such as OECD, IMF and World Bank had no influence in the study. From the findings, it is clear that the central government and state institutions lack the necessary resources to accelerate infrastructure development, water infrastructure in particular. The study, thus, recommends a complete expansion and development of state capacity as well as improved collaborations with the private sector to drive the success delivery of services to the public.

Improved and flexible regulations and legislative guidelines are required to ensure that both sectors fulfil their side of the bargain, with an ultimate goal of meeting the predetermined targets of supplying adequate water to the deprived communities.

Advances in Big Data, artificial Intelligence and data-driven innovation bring enormous benefits for the overall society and for different sectors. By contrast, their misuse can lead to data workflows bypassing the intent of privacy and data protection law, as well as of ethical mandates. It may be referred to as the ‘creep factor’ of Big Data, and needs to be tackled right away, especially considering that we are moving towards the ‘datafication’ of society, where devices to capture, collect, store and process data are becoming ever-cheaper and faster, whilst the computational power is continuously increasing. If using Big Data in truly anonymisable ways, within an ethically sound and societally focussed framework, is capable of acting as an enabler of sustainable development, using Big Data outside such a framework poses a number of threats, potential hurdles and multiple ethical challenges. Some examples are the impact on privacy caused by new surveillance tools and data gathering techniques, including also group privacy, high-tech profiling, automated decision making and discriminatory practices. In our society, everything can be given a score and critical life changing opportunities are increasingly determined by such scoring systems, often obtained through secret predictive algorithms applied to data to determine who has value. It is therefore essential to guarantee the fairness and accurateness of such scoring systems and that the decisions relying upon them are realised in a legal and ethical manner, avoiding the risk of stigmatisation capable of affecting individuals’ opportunities. Likewise, it is necessary to prevent the so-called ‘social cooling’. This represents the long-term negative side effects of the data-driven innovation, in particular of such scoring systems and of the reputation economy. It is reflected in terms, for instance, of self-censorship, risk-aversion and lack of exercise of free speech generated by increasingly intrusive Big Data practices lacking an ethical foundation. Another key ethics dimension pertains to human-data interaction in Internet of Things (IoT) environments, which is increasing the volume of data collected, the speed of the process and the variety of data sources. It is urgent to further investigate aspects like the ‘ownership’ of data and other hurdles, especially considering that the regulatory landscape is developing at a much slower pace than IoT and the evolution of Big Data technologies. These are only some examples of the issues and consequences that Big Data raise, which require adequate measures in response to the ‘data trust deficit’, moving not towards the prohibition of the collection of data but rather towards the identification and prohibition of their misuse and unfair behaviours and treatments, once government and companies have such data. At the same time, the debate should further investigate ‘data altruism’, deepening how the increasing amounts of data in our society can be concretely used for public good and the best implementation modalities.