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A policy of surveillance which interferes with the fundamental right to a private life requires credible justification and a supportive evidence base. The authority for such interference should be clearly detailed in law, overseen by a transparent process and not left to the vagaries of administrative discretion. If a state surveils those it governs and claims the interference to be in the public interest, then the evidence base on which that claim stands and the operative conception of public interest should be subject to critical examination. Unfortunately, there is an inconsistency in the regulatory burden associated with access to confidential patient information for non-health-related surveillance purposes and access for health-related surveillance or research purposes. This inconsistency represents a systemic weakness to inform or challenge an evidence-based policy of non-health-related surveillance. This inconsistency is unjustified and undermines the qualities recognised to be necessary to maintain a trustworthy confidential public health service. Taking the withdrawn Memorandum of Understanding (MoU) between NHS Digital and the Home Office as a worked example, this chapter demonstrates how the capacity of the law to constrain the arbitrary or unwarranted exercise of power through judicial review is not sufficient to level the playing field. The authors recommend ‘levelling up’ in procedural oversight, and adopting independent mechanisms equivalent to those adopted for establishing the operative conceptions of public interest in the context of health research to non-health-related surveillance purposes.

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.

Purpose – The purpose of this research is to study the ecological developments that are growing rapidly and are complemented by technological developments that make ecology a discipline which is able to collaborate, integrate, and use data for the development of science.

Design/Methodology/Approach – The method involves integration, analysis, and conclusion, drawing knowledge dissemination from heterogeneous ecological data that make the ecological research so complex requiring an approach to simplify the problem.

Findings – The data involved in ecology are very complex and diverse and spread from various sources, which are not mutually integrated so that a structured arrangement is required through the arrangement of computer-based data management.

Research Limitations/Implications – Eco-informatics is one of the options to manage the data, settings, and transform it into information and knowledge.

Justice perceptions describe an individual's evaluation of whether decisions or actions are fair or unfair. These perceptions are important because they affect individual attitudes and behaviors in different situations. Family firms develop and implement governance policies and structures (i.e., governance systems) to diminish the problems that can arise from the overlap between the business, the family, and the ownership systems of a firm. Governance systems help family firms have a clear structure of accountability and a clear understanding of the rights and responsibilities that family and non-family members have toward the family enterprise. Research on governance to date has focused on the practices and policies that exist and their effects on the family firm. However, in the governance context, individual perceptions are important because they are likely to affect the attitudes that family and other members have toward the family enterprise and the likelihood that they will follow the different policies when they are implemented. This chapter takes a receiver perspective to explain how individuals create justice perceptions based on governance mechanisms and the effects of these perceptions. The goal is to understand how we can use this information when developing governance practices in family firms.

Smart industry initiatives focus on intelligent and interconnected cyber-physical systems. These initiatives develop complex technical architectures that integrate heterogenous technologies, causing significant organizational complexity. Tapping into the digital capabilities of distant partners while capturing profit from such innovation is demanding. Furthermore, firms often need to establish and orchestrate inter-organizational collaborations without prior relations or established trust. As a result, smart industry initiatives bring together disparate organizational forms and institutional environments, distinctive knowledge bases, and geographically dispersed organizations. We conceptualize this organizational capability as ‘distant capabilities integration’. This research explores the governance mechanisms that support such integration and their relation to value capture. We analyse 11 IoT case studies organized in three categories (process, product and technologies) of smart industry initiatives. Building on existing literature, we consider different ways to describe distance, including knowledge heterogeneity and organizational, geographical, institutional, cultural and cognitive distance. Finally, we describe the governance mode appropriate for upstream (developing foundational technologies) and downstream (leveraging existing distant technologies) smart industry initiatives.

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.

Social relationships play an important role in organizational entrepreneurship. They are crucial to entrepreneurs’ decisions because, despite the bleeding-edge technological advancements observed nowadays, entrepreneurs as human beings will always strive to be social. During the COVID-19 pandemic many companies moved activities into the virtual world and as a result offline Social relationships became rarer, but as it turns out, even more valuable, likewise, the inter-organizational cooperation enabling many companies to survive.

This chapter aims to develop knowledge about entrepreneurs’ SR and their links with inter-organizational cooperation. The results of an integrative systematic literature review show that the concept of Social relationships, although often investigated, lacks a clear definition, conceptualization, and operationalization. This chapter revealed a great diversity of definitions for Social relationships, including different scopes of meaning and levels of analysis. The authors identify 10 building blocks and nine sources of entrepreneurs’ Social relationships. The authors offer an original typology of Social relationships using 12 criteria. Interestingly, with regard to building blocks, besides those frequently considered such as trust, reciprocity and commitment, the authors also point to others more rarely and narrowly discussed, such as gratitude, satisfaction and affection. Similarly, the authors discuss the varied scope of sources, including workplace, family/friendship, past relationships, and ethnic or religious bonds. The findings of this study point to a variety of links between Social relationships and inter-organizational cooperation, including their positive and negative influences on one another. These links appear to be extremely dynamic, bi-directional and highly complex.

“The unexamined life is not worth living” (Socrates). That is, without critically inquiring into the knowledge of life which is well-being and valuable, life is not worth living. Critical thinking questions existing theories and their unexamined and obsessive assumptions and generalizations, constraints, and “best” practices of the prevailing system of management and tries to replace them with more valid assumptions and generalizations that uphold the dignity, uniqueness, and inalienable rights of the individual person and the community. Better outcomes result from asking the right questions than from having the right answers. In the diverse, pluralist cultural environment of today, the promise of a truly generative dialog among Occidental (Western) and Oriental (Eastern) cultures and civilizations holds great hope for the future. Critical thinking (CT) is an “inclusive” thinking system that can facilitate this dialog such that all of us have a meaningful space and place in this universe. After defining CT and arguing its importance for executives, this chapter introduces CT in two parts: Part 1: Various Approaches to Critical Thinking; Part 2: Major Theories of Critical Thinking. Several contemporary business cases will be invoked to illustrate the need, nature, and scope of corporate CT.