This book has unique features that set it apart from conventional books on the prevention of system failures in that it provides a method that views human activities from a meta-methodological perspective based upon an inter-disciplinary understanding of human activities. With these characteristics, the book also proposes a common methodological basis to apply to various problems surrounding society today such as an aging social infrastructure; the safety of food, medicine, and public transportation; and the creation of sustainable electricity and cybersecurity. Furthermore, since the failure of human activities is expressed in a three-dimensional space and the topological metrics are implemented, the failure trajectories can be quantitatively monitored in time series to take effective preventive measures. Considering the implementation of the topological metrics, the causes of each failure are classified into two dimensions of the degree of coupling between system elements and the interaction between the target system and the external environment. Owing to the nature of the introduction of topological metrics, all individual and diverse systems can share general topological metrics. Consequently, understanding various failures over cross-industries is possible with the use of common meta-systemic language and mutual learning between different industries, and the solution of social problems can be effectively achieved. A system of system failures (SOSF) proposed and confirmed the effectiveness of this meta-methodology for information and communication technologies (ICT) systems and the SOSF is extended to human activity systems (SOHAS: system of human activity systems) as a whole. Therefore, the SOHAS becomes an academic foundation for theoretical research on meta-methodology, and it has an impact on practitioners to prevent system failures by accumulating knowledge of failures and learning from other industries.

In the last two decades, there has been growing interest in pursuing theoretical paradigms that capture complex learning situations. Cultural Historical Activity Theory (CHAT) is one of several theoretical frameworks that became very popular among educational researchers because it conceptualizes individuals and their environment as a holistic unit of analysis. It assumes a non-dualistic ontology and acknowledges the complexities involved in human activity in natural settings. Recently, reputable journals such as the American Psychologist, Educational Psychologist, and Educational Researcher that are targeted for a wide-range of audience have included articles on CHAT. In many of such articles, CHAT has been referred to as social constructivism, sociocultural theory, or activity theory. Activity systems analysis is one of the popular methods among CHAT researchers for mapping complex human interactions from qualitative data. However, understanding the methods involved in activity systems analysis is a challenging task for many researchers. This difficulty derives from several reasons. First the original texts of CHAT are in Russian and there have been numerous authors who report on the difficulties of reconciling translation problems of the works of original authors’ such as Vygotsky and Leontiev. Second, in North America activity systems analysis has deviated from the Russian scholars’ intentions and Engeström’s original work using the triangle model to identify tensions to overcome and bring about sociopolitical change in participant practices. Third, to this date there are numerous publications on the theoretical background of activity theory and studies reporting the results of using activity systems analysis for unpacking qualitative data sets, but there have been no methodological publications on how researchers engage in activity systems analysis. Thus, there is a dearth of literature in both book and journal publications that guide researchers on the methodological issues involving activity systems analysis.

In providing a theoretical framework for understanding human- computer interaction as well as design of user interfaces, this book combines elements of anthropology, psychology, cognitive science, software engineering, and computer science. The framework examines the everyday work practices of users when analyzing and designing computer applications. The text advocates the unique theory that computer application design is fundamentally a collective activity in which the various practices of the participants meet in a process of mutual learning.

Soft Systems Methodology in Action "Whether by design, accident or merely synchronicity, Checkland appears to have developed a habit of writing seminal publications near the start of each decade which establish the basis and framework for systems methodology research for that decade." Hamish Rennie, Journal of the Operational Research Society, 1992 Thirty years ago Peter Checkland set out to test whether the Systems Engineering (SE) approach, highly successful in technical problems, could be used by managers coping with the unfolding complexities of organizational life. The straightforward transfer of SE to the broader situations of management was not possible, but by insisting on a combination of systems thinking strongly linked to real-world practice Checkland and his collaborators developed an alternative approach - Soft Systems Methodology (SSM) - which enables managers of all kinds and at any level to deal with the subtleties and confusions of the situations they face. This work established the now accepted distinction between 'hard' systems thinking, in which parts of the world are taken to be 'systems' which can be 'engineered', and 'soft' systems thinking in which the focus is on making sure the process of inquiry into real-world complexity is itself a system for learning. Systems Thinking, Systems Practice (1981) and Soft Systems Methodology in Action (1990) together with an earlier paper Towards a Systems-based Methodology for Real-World Problem Solving (1972) have long been recognized as classics in the field. Now-Peter Checkland has looked back over the three decades of SSM development, brought the account of it up to date, and reflected on the whole evolutionary process which has produced a mature SSM. SSM: A 30-Year Retrospective, here included with Soft Systems Methodology in Action closes a chapter on what is undoubtedly the most significant single research programme on the use of systems ideas in problem solving. Now retired from full-time university work, Peter Checkland continues his research as a Leverhulme Emeritus Fellow.

This fully revised and updated second edition of Information Systems Strategic Management continues to provide an accessible yet critical analysis of the strategic aspects of information systems. The second edition again covers the relevant practical and theoretical material of information systems, supported by extensive case studies, student activities, and problem scenarios. The ISS issues will be fully integrated into current thinking about corporate strategy, addressing the fact that a range of emerging strategic issues are often ill addressed in IS strategy books, which also fail to differentiate between IT, the application of technology, and IS, the participative, human-centred approaches to information and knowledge management. Specific changes include Expansion and internationalisation of case studies Broader focus beyond social and critical theory New chapters on strategy and e-business, strategic management as a technical or social process, strategic implications of information security, applications portfolio, and technology management. The focus on strategic issues and the integration of IT and IS issues ensures this text is ideal for MBA students studying MIS, as well as being suitable for MSC students in IS/IT.

Engineering Intelligent Systems Exploring the three key disciplines of intelligent systems As artificial intelligence (AI) and machine learning technology continue to develop and find new applications, advances in this field have generally been focused on the development of isolated software data analysis systems or of control systems for robots and other devices. By applying model-based systems engineering to AI, however, engineers can design complex systems that rely on AI-based components, resulting in larger, more complex intelligent systems that successfully integrate humans and AI. Engineering Intelligent Systems relies on Dr. Barclay R. Brown’s 25 years of experience in software and systems engineering to propose an integrated perspective to the challenges and opportunities in the use of artificial intelligence to create better technological and business systems. While most recent research on the topic has focused on adapting and improving algorithms and devices, this book puts forth the innovative idea of transforming the systems in our lives, our societies, and our businesses into intelligent systems. At its heart, this book is about how to combine systems engineering and systems thinking with the newest technologies to design increasingly intelligent systems. Engineering Intelligent Systems readers will also find: An introduction to the fields of artificial intelligence with machine learning, model-based systems engineering (MBSE), and systems thinking—the key disciplines for making systems smarter An example of how to build a deep neural network in a spreadsheet, with no code or specialized mathematics required An approach to the visual representation of systems, using techniques from moviemaking, storytelling, visual systems design, and model-based systems engineering An analysis of the potential ability of computers to think, understand and become conscious and its implications for artificial intelligence Tools to allow for easier collaboration and communication among developers and engineers, allowing for better understanding between stakeholders, and creating a faster development cycle A systems thinking approach to people systems—systems that consist only of people and which form the basis for our organizations, communities and society Engineering Intelligent Systems offers an intriguing new approach to making systems more intelligent using artificial intelligence, machine learning, systems thinking, and system modeling and therefore will be of interest to all engineers and business professionals, particularly systems engineers.