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Civil society is increasingly digitized and virtual in many parts of the globalized world of today. The networked society and the invisible “second economy” (Arthur, 2011) which powers the developed and developing countries generate debates about the degree to which the benefits outweigh the potential hazards. Artificial intelligence (AI) powered by its machine learning underpin much of the digital networked systems, and “free” services such as search engines, paid for by the “tailored advertising” we get when we view webpages. Most now recognize that the helpful “suggestions” on the web are simply adverts personally targeted at individuals who have searched for information on a topic or visited a webpage with sponsored material and cookies.

There have been cases of major political misuse of data such as the voter manipulation by the Cambridge Analytica company. We are not just referring to the hacking and “fake news” used by some governments to influence the affairs of another country. Some organizations have used AI to cynically target consumers’ weaknesses, for example, in financial management (Larsson, 2018).

Perhaps more significantly the network technology is often promoted as having potential for improving civil society through “failsafe” or default forms of regulation using the embedded Apps in domestic equipment and algorithms in much the same manner it is suggested that automatic self-driving vehicles help to improve road safety by cautious driving and sticking to speed limits and so on (Cockburn, Jahdi, & Wilson, 2015, pp. 6–7). However, algorithms and the associated machine technology have also been described as a “black box” technology where even those people running the algorithms cannot always fully understand or explain how decisions are reached in diverse systems used to evaluate many things from medical care to credit rating and finance (Danaher et al., 2017). There are issues of the budding “surveillance society” emerging from the proliferating “intelligent” apps enabling corporate “spying” on our everyday lives as some hackers have done by tapping into baby monitoring systems in homes. In addition to hacking there are large power asymmetries involved as between commercial data users and the lay public who are often the data suppliers as their personal data are harvested each time the web is used.

Therefore, it is hardly surprising that, according to the Pew Research Center report by Aaron Smith, released in November 2018, over half of Americans surveyed found it unacceptable to use algorithms to make decisions with real-world consequences for humans. In the age of connectedness and the emergent internet of things many people are not yet ready to cede more control of their currently offline lives to current online technology. This chapter reviews arguments for and against algorithmic governance.

Machine learning systems may be efficient to a high degree without being unbiased in impact across different segments of society. AI may also be fully effective in its operation without even being fully understood because the decision-making is so arcane. Importantly, though, even for those systems that have some human mediation or supervision, societal regulation is aimed at ensuring ends and means are aligned with human social, political and economic justice and thus socially effective as well as being technically efficient. Consequently, these systems have to require socio-emotional as well as cognitive safeguards. Although levels of implicit trust may vary demographically as between say millennials and baby boomers, high levels of trust, accountability and a culture of moral integrity must still form the bedrock for societal benefits.

This chapter emphasizes the importance of potential and realized capabilities in building and sustaining social innovations. We present an assessment of the drivers and barriers to the development of social innovation ecosystems through the use of internal (absorptive capacities) and external organizational capabilities (open social innovation). A particular emphasis is placed on social innovation outcomes and impacts. The capability approach is particularly useful for measuring social innovation impacts and investigating the “micro-meso-macro” of linkages that underlie the process of social innovation.

Our methodological approach measures social innovations’ outcomes and impact through an aggregative model, which takes into consideration different forms of capabilities that are engendered in the process of or as a result of social innovation. This methodology highlights the importance of social innovation drivers and contexts in which knowledge exploration and exploitation lead to the creation of capabilities that help social innovators to respond to unfulfilled social needs.

The research presented in this chapter provides a new perspective on how the capability approach can be used to assess and measure individual and collective actions when facing social challenges.

Our research supports and complements the findings of the EU project SIMPACT an acronym for “Boosting the Impact of SI in Europe through Economic Underpinnings,” by highlighting the processes and outcomes and SI impact measurement.

The measurement of social innovation outcomes and impact has gained importance for policy makers and constitutes a strategic tool for designing policies in support of social entrepreneurs, social investors, and private and public organizations which participate in cocreating and implementing innovative projects.

The research presented in this chapter sheds light on the “micro-meso-macro” linkages that foster social innovation ecosystems, and offer valuable tools and guidelines to researchers, practitioners, and policymaker in the field of innovation in general and social innovation in particular.

Almost every detail of our lives – where we go, what we do, and with whom – is captured as digital data. Technological advancements in cloud computing, artificial intelligence, and data analytics offer the education sector new ways not only to improve policy and processes but also to personalize learning and teaching practice. However, these changes raise fundamental questions around who owns the data, how it might be used, and the consequences of use. The application of Big Data in education can be directed toward a wide range of stakeholders, such as educators, students, policy-makers, institutions, or researchers. It may also have different objectives, such as monitoring, student support, prediction, assessment, feedback, and personalization. This chapter presents the nuances and recent research trends spurred by technological advancements that have influenced the education sector and highlights the need to look beyond the technical boundaries using a socio-semiotic lens. With the explosion of available information and digital technologies pervading cultural, social, political as well as economic spaces, being a lifelong learner is pivotal for success. However, technology on its own is not sufficient to drive this change. For technology to be successful, it should complement individual learning cultures and education systems. This chapter is broadly divided into two main sections. In the first section, we contemplate a vision for the future, which is deemed possible based on ongoing digital and computing advancements. The second section elaborates the technological, pedagogical, cultural, and political requirements to attain that vision.

Chapter 4 addresses the importance of internal knowledge cultures. It provides a deeper dive into how internal knowledge cultures can contribute to a company’s performance. The authors explain how knowledge culture shapes practical knowledge processes and fosters intellectual capital. The authors also provide insights into a critical knowledge paradox and discuss the interplay of knowledge paradoxes and cultural collisions. The chapter is supported by practical use cases that illustrate the points in the chapter.

In today's internationalized world, value creation consists of knowledge and work integration involving workers from around the world. Members of these globally distributed work teams (GDWT) encounter organizational behavior issues (identity, cultural differences, and leadership) and organization design issues (dependencies, information processing, media use, and teamwork structures). While most research on GDWT focuses on the first set of issues, this chapter is among the few to systematically explore the second set. We propose and elaborate on strategies for either reducing the intensity of collaboration, or enabling teams to collaborate intensely on a global scale. Implications for research and practice are explored.

Theories of platform strategy and adoption have been largely derived from studies of their application in the information and communications technology (ICT) sector. These platforms vary in openness, with the model of open source software providing the best-known exemplar for open platforms.

This exploratory field study examines the degree to which nine attributes of ICT platforms are applicable to open platforms in biotechnology. Using a combination of interview and secondary data, it identifies three patterns of such biotechnology platforms – IP commons, hackerspaces, and crowdsourced patient registries – and the degree to which these nine attributes apply. It shows the impact of ICT platforms and open source software on open source approaches to biotechnology, and how the latter are affected by the technical, legal, and institutional differences between information technology and biotechnology.

Instead of open source software platforms organized around modular interfaces, complements, ecosystems, and two-sided markets, this study instead suggests a model of open source knowledge platforms which benefits from economies of scale but not indirect network effects. From this, it discusses the generalizability of the ICT-derived models of open source platforms and offers suggestions for future research.