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The purpose of this paper is clearly illustrate this convergence and the prescriptive recommendations that such documents entail. There is a significant amount of research into the ethical consequences of artificial intelligence (AI). This is reflected by many outputs across academia, policy and the media. Many of these outputs aim to provide guidance to particular stakeholder groups. It has recently been shown that there is a large degree of convergence in terms of the principles upon which these guidance documents are based. Despite this convergence, it is not always clear how these principles are to be translated into practice.

In this paper, the authors move beyond the high-level ethical principles that are common across the AI ethics guidance literature and provide a description of the normative content that is covered by these principles. The outcome is a comprehensive compilation of normative requirements arising from existing guidance documents. This is not only required for a deeper theoretical understanding of AI ethics discussions but also for the creation of practical and implementable guidance for developers and users of AI.

In this paper, the authors therefore provide a detailed explanation of the normative implications of existing AI ethics guidelines but directed towards developers and organisational users of AI. The authors believe that the paper provides the most comprehensive account of ethical requirements in AI currently available, which is of interest not only to the research and policy communities engaged in the topic but also to the user communities that require guidance when developing or deploying AI systems.

The authors believe that they have managed to compile the most comprehensive document collecting existing guidance which can guide practical action but will hopefully also support the consolidation of the guidelines landscape. The authors’ findings should also be of academic interest and inspire philosophical research on the consistency and justification of the various normative statements that can be found in the literature.

The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability to complete tasks through the cooperation of many agents. In this study, the evolution of crowd intelligence is studied through the clustering method and the particle swarm optimization (PSO) algorithm.

This study proposes a crowd evolution method based on intelligence level clustering. Based on clustering, this method uses the agents’ intelligence level as the metric to cluster agents. Then, the agents evolve within the cluster on the basis of the PSO algorithm.

Two main simulation experiments are designed for the proposed method. First, agents are classified based on their intelligence level. Then, when evolving the agents, two different evolution centers are set. Besides, this paper uses different numbers of clusters to conduct experiments.

The experimental results show that the proposed method can effectively improve the crowd intelligence level and the cooperation ability between agents.

This paper proposes a crowd evolution method based on intelligence level clustering, which is based on the clustering method and the PSO algorithm to analyze the evolution.

This chapter presents the many premises of this book. It first discusses the book’s central questions and lays out the design of the large multi-national and multi-method study, carried out across Northern Europe. It also places the book at the interdisciplinary space between contemporary innovation economics and cultural and social theory. It then discusses the complex set of social processes that have conditioned the phenomena that the book studies – how and why are the contemporary audiovisual media industries co-innovating and converging with other sectors including education, tourism and health care? Within this framework, it discusses the effects of the broader individualisation and mediatisation processes, of media convergence, of the emergence of cross-media or transmedia strategies, of the evolution of the service and experience economies and of the emergence of creative industries policy frameworks.

This paper explores the convergence of Education 4.0 and Industry 4.0 and presents a Twin Peaks model for their seamless integration.

A high-level literature review is conducted to identify and discuss the important challenges and opportunities offered by both Education 4.0 and Industry 4.0. A novel Twin Peaks model is devised for the convergence of these domains and to cope with the challenges effectively.

The proposed Twin Peak model for the convergence of Education 4.0 and Industry 4.0 suggests that the development of these two domains is interdependent. It emphasizes ethical considerations, inclusivity and understanding the concerns of stakeholders from both education and industry. We have also explained how continuous incremental adaptation within the proposed Twin Peaks model might assist in addressing concerns of one sector with the opportunities of the other.

First, Education 4.0 and Industry 4.0 are reviewed in terms of opportunities and challenges they present. Second, a novel Twin Peaks model for the convergence of Education 4.0 and Industry 4.0 is presented. The proposed discovers that the convergence is adaptive, iterative and must be ethically sound while considering the broader societal implications of the digital transformation. Third, this study also acts as a torch-bearer for the necessity for more research of this kind to guarantee that our educational ecosystem is adaptable and capable of producing the skills required for success in the era of IR4.0.

This paper aims to present a novel pedagogical model that aims at bridging creativity with computational thinking (CT) and new media literacy skills at low-technology, information-rich learning environments. As creativity, problem solving and collaboration are among the targeted skills in twenty-first century, this model promotes the acquisition of these skills towards a holistic development of students in primary and secondary school settings. In this direction, teaching students to think like a computer scientist, an economist, a physicist or an artist can be achieved through CT practices, as well as media arts practices. The interface between these practices is imagination, a fundamental concept in the model. Imaginative teaching methods, computer science unplugged approach and low-technology prototyping method are used to develop creativity, CT, collaboration and new media literacy skills in students. Furthermore, cognitive, emotional, physical and social abilities are fostered. Principles and guidelines for the implementation of the model in classrooms are provided by following the design thinking process as a methodological tool, and a real example implemented in a primary school classroom is described. The added value of this paper is that it proposes a pedagogical model that can serve as a pool of pedagogical approaches implemented in various disciplines and grades, as CT curriculum frameworks for K-6 are still in their infancy. Further research is needed to define the point at which unplugged approach should be replaced or even combined with plugged-in approach and how this proposed model can be enriched.

This paper presents a pedagogical model that aims at bridging creativity with CT, collaboration and new media literacy skills.

The proposed model follows a pedagogy-driven approach rather a technology-driven one as the authors suggest its implementation in low-tech, information-rich learning environments without computers. The added value of this paper is that it proposes a novel pedagogical model that can serve as a pool of pedagogical approaches and as a framework implemented in various disciplines and grades. A CT curriculum framework for K-6 is an area of research that is still in its infancy (Angeli et al., 2016), so this model is intended to provide a holistic perspective over this area by focusing how to approach the convergence among CT, collaboration and creativity skills in practice rather than what to teach. Based on literature, the authors explained how multiple moments impact on CT, creativity and collaboration development and presented the linkages among them. Successful implementation of CT requires not only computer science and mathematics but also imaginative capacities involving innovation and curiosity (The College Board, 2012). It is necessary to understand the CT implications for teaching and learning beyond the traditional applications on computer science and mathematics (Kotsopoulos et al., 2017) and start paying more attention to CT implications on social sciences and non-cognitive skills. Though the presented example (case study) seems to exploit the proposed multiple moments model at optimal level, empirical evidence is needed to show its practical applicability in a variety of contexts and not only in primary school settings. Future studies can extend, enrich or even alter some of its elements through experimental applications on how all these macro/micromoments work in practice in terms of easiness in implementation, flexibility, social orientation and skills improvement.

The added value of this paper is that it joins learning theories, pedagogical methods and necessary skills acquisition in an integrated manner by proposing a pedagogical model that can orient activities and educational scenarios by giving principles and guidelines for teaching practice.

Applying both the dynamic capability and configuration theoretical perspectives, the paper showcases the role of network configuration and dynamics of hybrid offerings in both developed and emerging markets by high-tech firms.

The current paper uses an exploratory qualitative research methodology based on in-depth case studies of three Finnish high-tech firms operating in the medical technology industry globally.

The findings from the study showed that dynamic capabilities such as sensing and customer engagement along with internal coordination and adaptation capabilities are critical to the success of hybrid market offerings. Moreover, dynamic capabilities were found to be influential in those emerging and advanced international markets where case firms were less familiar with market dynamics. Moreover, the configuration of these capabilities within functional units and coordination of marketing and R&D activities can be effective for creating hybrid offerings in international markets. Ultimately, this was found to be the case even though target market selection for hybrid offerings was influenced by the level of convergence and fragmentation of the market.

Applying the configuration theory, this is one of the first studies to specifically analyze the differences in organizational network configuration changes in relation to hybrid market offerings in both developed economies and emerging economies. The findings contribute to hybrid market offering literature by pointing out that not only internal capabilities are important for enacting hybrid offerings, but the roles of ecosystems and knowledge centers are also extremely important to develop hybrid offerings. This paper also highlights the criticality of under-studied dynamic capabilities such as market sensing and customer engagement in the context of hybrid offerings in international markets. This showcases the wider role of ecosystems in enabling technology firms to develop hybrid offerings.

With the development of machine learning techniques, the artificial intelligence systems such as crowd networks are becoming more autonomous and smart. Therefore, there is a growing demand for developing a universal intelligence measurement so that the intelligence of artificial intelligence systems can be evaluated. This paper aims to propose a more formalized and accurate machine intelligence measurement method.

This paper proposes a quality–time–complexity universal intelligence measurement method to measure the intelligence of agents.

By observing the interaction process between the agent and the environment, we abstract three major factors for intelligence measure as quality, time and complexity of environment.

This paper proposes a calculable universal intelligent measure method through considering more than two factors and the correlations between factors which are involved in an intelligent measurement.

This study aims to investigate how two collaborative methods – selection and synthesis – influence knowledge convergence when people articulate a new strategic direction driving transformation within the organization.

The study is based on a longitudinal field experiment developed in four organizations involving 82 employees over a three-month process. Inspired by dynamics governing flocks as complex adaptive systems, selection and synthesis have been separately used in two sets of companies. Primary and secondary data have been largely collected and analyzed throughout the whole process.

This study describes how the two alternative methods differently influenced two kinds of knowledge convergence. While selection triggers a general and static knowledge convergence and the propagation of individual knowledge over time, synthesis fosters a local and dynamic knowledge convergence where individuals tend to propagate knowledge generated collectively.

This research offers insights into understanding the influence of alternative collaborative methods on the creation and propagation of knowledge when people are converging toward a new strategic direction. From a theoretical perspective, it contributes to complex adaptive system theory, highlighting the role of knowledge convergence and emergence through collaboration.

This research offers insights to managers who deal with the complexity of the engagement of different stakeholders during collaborative processes, offering some actionable takeaways to foster knowledge convergence by alternatively employing selection and synthesis.

This paper contributes to the management and social information processing literature emphasizing the role of knowledge convergence emerging from the complex interactions among multiple stakeholders.

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.

The rapid development of 5G technology brings the expansion of the internet of things (IoT). A large number of devices in the IoT work independently, leading to difficulties in management. This study aims to optimize the member structure of the IoT so the members in it can work more efficiently.

In this paper, the authors consider from the perspective of crowd science, combining genetic algorithms and crowd intelligence together to optimize the total intelligence of the IoT. Computing, caching and communication capacity are used as the basis of the intelligence according to the related work, and the device correlation and distance factors are used to measure the improvement level of the intelligence. Finally, they use genetic algorithm to select a collaborative state for the IoT devices.

Experimental results demonstrate that the intelligence optimization method in this paper can improve the IoT intelligence level up to ten times than original level.

This paper is the first study that solves the problem of device collaboration in the IoT scenario based on the scientific background of crowd intelligence. The intelligence optimization method works well in the IoT scenario, and it also has potential in other scenarios of crowd network.