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The circumstances in which everyday life takes place shapes people’s capacity to lead healthy lives. We posit that the traditional linear way of thinking about causes and consequences limits our ability to fully capture the complexity of the interaction between people and their contextual circumstances in the production of health. We view critical realism as particularly helpful in understanding how problems are generated by this interaction as well as understanding the fit and responsiveness of our interventions to situational contexts. Such a perspective requires systematically attempting to “unpack” how different settings are shaped by health promotion interventions and how this is experienced by people. Accessing and mobilising such knowledge also requires particular sets of methods that deviate from what is still often seen as the gold standard for designing and evaluating interventions. Critical realism therefore shifts the primary focus of health promotion research from questions about “if” interventions work to questions about “how”, “why” and “under what circumstances” they can work. By helping to capture complexity in health promotion intervention research, a critical realist perspective leads to the production of rigorous knowledge without having to succumb to concerns about standardisation.

Innovation challenges are increasingly complex, cutting across distributed actors from different disciplines, organizations, and fields. Solving such challenges requires creating the capacities of opening up for innovation to access and develop a greater amount and variety of knowledge and resources. Perspectives on open source, open innovation, and interorganizational collaboration have explored such capacities, but from different origins and scopes of analysis. Our practice-based integrative framework of “opening innovation” helps highlight these differences and connect their relative strengths. Through a critical literature review paired with an analysis of different empirical cases from Hacking Health, a non-profit organization helping drive digital health innovation, the authors reveal the user-centric, firm-centric, and field-centric approaches to opening innovation that progressively connect a greater variety of actors and resources. The authors show how specific new relational practices they produce address the new relational dynamics these connections bring to accumulate more resources for innovation to keep progressing.

La gestion est-elle un mal ou un remède pour les entreprises sociales et solidaires ? Les entreprises sociales et solidaires sont-elles des modèles d’apprentissage pour la gestion ? Nous amènent-elles à penser la gestion autrement ? Cet ouvrage vise à dépasser les tabous liés à la gestion dans l’entreprise sociale et solidaire. Collectif de chercheurs en sciences humaines et sociales (académiques et/ou praticiens), notre ambition est de porter un regard critique sur la gestion des entreprises sociales et solidaires. Sur la base de l’étude de nombreux cas (mutuelles, associations, coopératives de consommateurs, banques coopératives, Scop, Scic, etc.), il s’agit de questionner et comprendre les dispositifs et les pratiques de gestion des entreprises sociales et solidaires. La réflexion des auteurs s’est construite autour des questionnements suivants : Que nous apprennent les entreprises sociales et solidaires sur la gestion des organisations ? Qu’ont-elles mis en œuvre de spécifique ? Existe-t-il déjà des « pépites » à observer, à essaimer issues de leurs pratiques de gestion ? Le phénomène d’isomorphisme avec les modèles d’entreprise capitaliste est-il si important ? Si oui, est-il un problème ? Pourquoi ? Et comment construire d’autres modes de gestion ? Quelles questions les organisations doivent-elles se poser pour dépasser les tensions inhérentes à l’hybridité entre économique, social ou solidaire ? Que doivent-elles inventer ? L’ouvrage se compose d’essais qui visent à défendre des points de vue sur des sujets récurrents et importants pour les entreprises sociales et solidaires. Ces derniers sont organisés en quatre thèmes : dépasser les tabous pour une gestion utile au projet social ou solidaire ; gestion pour et par la valeur sociale ; comment organiser durablement la gouvernance démocratique ; penser autrement la gestion des ressources humaines dans l’entreprise sociale et solidaire. Ces questions, nous l’espérons, feront sens et aideront tant dans la compréhension des phénomènes que dans la prise de décisions et la formation pour une gestion au service des entreprises sociales et solidaires.

This chapter introduces responsible innovation in a business context. The first part explains the basic terms that constitute responsible innovation from a business perspective. The second part presents tangible business practices that operationalise responsible innovation and introduces two good practice examples that hint at the variety of ways in which responsible innovation can be implemented in companies.

I provide a vision and definition of Responsible Research and Innovation and propose a broad framework for its implementation under Research and Innovation schemes around the world. I make the case that RRI should be understood as a strategy of stakeholders to become mutual responsive to each other and anticipate research and innovation outcomes underpinning the "grand challenges" of our time for which they share > responsibility.> Research and Innovation processes need to become more responsive and adaptive to these grand challenges. This implies, among other, the introduction of broader foresight and impact assessments for new technologies beyond their anticipated market-benefits and risks. Social benefits of new technologies need to take into account widely shared public values. This implies a paradigm shift in innovation policy, moving away from an emphasis on key technologies towards issue and mission oriented policies. Background information can be found on: http://Renevonschomberg.wordpress.com

This chapter focuses on co-creation as the way to engage different stakeholders with everyday urban environments based on equality, diversity and social cohesion. It presents the relationship of co-creation and inclusiveness of public open spaces together with different aspects of co-creation related to issues of publicness and space. It discusses why and how co-creation must take into consideration the characteristics of the comprehensive spatial development processes. It suggests that co-creation is a wider concept than co-design and is a multistage process that contributes to inclusive public spaces, providing measures for social sustainability of place. This chapter argues that digital tools may help to overcome challenges of co-creation and provide an opinion on the contribution of digital technologies to the co-creation process by engaging people in the design, use and management of public spaces, providing new resources for interaction and users’ empowerment. For that it presents an overview of the possible contribution of digital technologies to support inclusiveness of the co-creation processes that is structured by typologies of digital tools and their possible interlinking with the steps of the co-creation process. To improve the understanding of such possibilities it critically addresses strengths and weaknesses of using digital tools for co-creation and inclusiveness and provides recommendations for their further development.

This chapter considers the role of universities in stimulating social innovation, and in particular the issue that despite possessing substantive knowledge that might be useful for stimulating social innovation, universities to date have not been widely engaged in social innovation activities in the context of Quadruple Helix developmental models. We explain this in terms of the institutional logics of engaged universities, in which entrepreneurial logics have emerged in recent decades, that frame the desirable forms of university-society engagement in terms of the economic benefits they bring. We ask whether institutional logics could explain this resistance of universities to social innovation. Drawing on two case studies of universities sincerely committed to supporting social innovation, we chart the effects of institutional logics on university-supported social innovation. We observe that there is a “missing middle” between enthusiastic managers and engaged professors, in which four factors serve to undermine social innovation activities becoming strategically important to HEIs. We conclude by noting that this missing middle also serves to segment the operation of Quadruple Helix relationships, thereby undermining university contributions to societal development more generally.

Face à la conception technocratique et entrepreneuriale portée par les pouvoirs publics, une approche alternative de l’innovation sociale, plus populaire et moins visible, à travers l’exploration d’initiatives citoyennes. Prenant comme point de départ le constat d’une appropriation institutionnelle de l’innovation sociale, orientée vers la compétitivité et l’efficacité marchande des expériences de l’économie sociale et solidaire, l’ouvrage vise à la fois à apporter un regard critique sur cette conception de l’innovation sociale et à remettre en lumière des expérimentations citoyennes peu prises en compte par les pouvoirs publics. Il montre ainsi la nécessité d’un tournant épistémologique valorisant les dynamiques de coproduction des savoirs et des politiques entre acteurs, chercheurs et institutions.

La transformation numérique et l’innovation collaborative ou les notions associées « d’intelligence collective », de « design thinking », « d’agilité » sont en passe de devenir les principaux concepts à la mode du management dans les organisations privées et publiques, au moins au sein des sièges et des directions centrales. Partant d’une description des spécificités des bouleversements introduits par la transition numérique et des technologies capacitantes qu’elle promeut, nous montrons comment les opérateurs sont parfois en demande de plus d’innovations numériques pour améliorer leurs conditions de travail et les services rendus au public, pour autant que celles-ci ne soient pas substitutives et excessivement rationalisantes. Les démarches d’innovation collaborative, soutenues par le haut management de manière parfois paradoxale, contribuent à faciliter ces mutations.

Technology is the answer, but what was the question? Introduction Many firms, charities and governments are in favour of more innovation, and like to side with the new against the old. But should they? A moment's reflection shows that it's not altogether coherent (whether intellectually, ethically or in terms of policy) to simply be in favour of innovation, whether that innovation is a product, a service or a social idea. Some innovations are unambiguously good (like penicillin or the telephone). Others are unambiguously bad (like concentration camps or nerve gas). Many are ambiguous. Pesticides kill parasites but also pollute the water supply. New surveillance technologies may increase workplace productivity but leave workers more stressed and unhappy. Smart missiles may be good for the nations deploying them and terrible for the ones on the receiving end.In finance, Paul Volcker, former head of the US Federal Reserve, said that the only good financial innovation he could think of was the automated teller machine. That was an exaggeration. But there is no doubt that many financial innovations destroyed more value than they created, even as they enriched their providers, and that regulators and policy makers failed to distinguish the good from the bad, with very costly results. In technology, too, a similar scepticism had emerged by the late 2010s, with digital social media described as the ‘new tobacco’, associated with harm rather than good, with addiction rather than help. Or, to take another example: when the US Central Intelligence Agency's venture capital arm, In-QTel, invested heavily in firms like Palantir, which then became contractors for the intelligence and military (a prime example of the ‘entrepreneurial state’), it was far from obvious how much this was good or bad for the world.The traditional justification for a capitalist market economy is that the net effects of market-led innovation leave behind far more winners than losers, and that markets are better able to pick technologies than bureaucracies or committees. But even if, overall, the patterns of change generate more winners than losers, there are likely to be some, perhaps many, cases where the opposite happens. It would be useful to know.

The concept of co-creation includes a wide range of participatory practices for design and decision making with stakeholders and users. Generally co-creation refers to a style of design or business practice characterized by facilitated participation in orchestrated multi-stakeholder engagements, such as structured workshops and self-organizing modes of engagement. Co-creation envelopes a wide range of skilled social practices that can considerably inform and enhance the effectiveness of organizational development, collaboration, and positive group outcomes. New modes of co-creation have emerged, evolving from legacy forms of engagement such as participatory design and charrettes and newer forms such as collaboratories, generative design, sprints, and labs. Often sessions are structured by methods that recommend common steps or stages, as in design thinking workshops, and some are explicitly undirected and open. While practices abound, we find almost no research theorizing the effectiveness of these models compared to conventional structures of facilitation. As co-creation approaches have become central to systemic design, service design, and participatory design practices, a practice theory from which models might be selected and modified would offer value to practitioners and the literature. The framework that follows was evolved from and assessed by a practice theory of dialogic design. It is intended to guide the development of principles-based guidelines for co-creation practice, which might methodologically bridge the wide epistemological variances that remain unacknowledged in stakeholder co-creation practice.

L’innovation sociale est largement considérée comme vertueuse. Cependant, le consensus qui semble régner en la matière vient de ce que les représentations et les pratiques englobées sous ce terme recouvrent un faisceau très diversifié d’approches et de réalités. Cette polysémie permet à de nombreux auteurs de se ranger sous une même bannière alors qu’ils ont des références et des orientations distinctes, voire divergentes. L’éloge unanime de l’innovation sociale ne saurait donc faire illusion. À cet égard, un travail introductif autour de l’innovation sociale a mis en évidence deux acceptions contrastées. La première version, qui peut être qualifiée de faible, aménage le système existant, insiste sur l’importance de l’épreuve marchande et valorise l’entreprise privée dans sa capacité à trouver de nouvelles solutions aux problèmes de société. La seconde version, qui peut être désignée comme forte, affiche une visée transformatrice ; elle prône, en réaction à la démesure du capitalisme marchand, une articulation inédite entre pouvoirs publics et société civile pour répondre aux défis écologiques et sociaux. La première se contente d’une amélioration du modèle économique dominant, l’innovation s’inscrivant dans une perspective réparatrice et fonctionnelle, tandis que la seconde a pour caractéristique un questionnement critique de ce modèle, et a pour horizon une démocratisation de la société.

In this chapter I turn to how social science can be adapted to the challenges and tools of the 2020s, becoming more data driven, more experimental and fuelled by more dynamic feedback between theory and practice. Social science at its grandest is the way societies understand themselves: why they cohere or fall apart; why some grow and others shrink; why some care and others hate; how big structural forces explain the apparently special facts of our own biographies. It observes but also shapes action, and then learns from those actions.Starting with the idea of social science as collective selfknowledge, I describe how new approaches to intelligence of all kinds can help to reinvigorate it. I begin with data and computational social science and then move on to cover the idea of social R&D and experimentation, new ways for universities to link into practice, including social science parks, accelerators tied to social goals, challenge-based methods and social labs of all kinds, before concluding with the core argument: an account of how social science can engage with the emerging field of intelligence design. This is, I hope, a plausible and desirable direction of travel.The rise of data-driven and computational social ScienceWe are all familiar with the extraordinary explosion of new ways to observe social phenomena, which are bound to change how we ask social questions and how we answer them. Each of us leaves a data trail of whom we talk to, what we eat and where we go. It's easier than ever to survey people, to spot patterns, to scrape the web, to pick up data from sensors or to interpret moods from facial expressions. It's easier than ever to gather perceptions and emotions as well as material facts – for example, through sentiment analysis of public debates. And it's easier than ever for organisations to practise social science – whether it's investment organisations analysing market patterns, human resources departments using behavioural science or local authorities using ethnography.These tools are not monopolised by professional social scientists. In cities, for example, offices of data analytics link multiple data sets and governments use data to feed tools using AI – like Predpol or HART – to predict who is most likely to go to hospital or end up in prison.

This chapter is about evidence and whether we can, or should, know our impact, the effect we have in the world. It addresses the difficulties as well as the possibilities of evidence for innovators and politicians, civil servants and head teachers, charities and doctors. I also touch on the question at the level of daily life, the moral question of whether we help those around us to be healthier, happier and more prosperous. Knowing our own impacts is, I argue, as much a moral prerogative as the traditional philosophical injunction of knowing ourselves.The enlightenment storyMany of us imbibed from an early age what can be called the enlightenment story. In this story new knowledge is steadily accumulated, mainly in universities and from academic journals. Theories are invented, tested, refuted and then improved. Scepticism helps to refine them and, as Wittgenstein wrote, the child first learns belief and only then learns doubt. You could say that at school we learn knowledge, and then at university we learn to question that knowledge.Belief is strengthened precisely because it has already been knocked down. And so, accumulating knowledge shows that this medicine, that economic policy or this teaching method works and many others don’t. The successful method then spreads, because when you design a better mousetrap the world beats a path to your door. It spreads because people are rational and want to do better and are persuaded by evidence. And so, the world progresses. Light replaces darkness. Effective solutions displace failed ones.It's easy to mock the enlightenment story. The sociologists of science have shown a much messier pattern of change – full of barriers, wilful resistance and peer pressure. But the old enlightenment story contains a good deal of truth and is preferable to the alternatives. Because of intense pressures to act on evidence, and habits of doubt among maintenance staff and engineers, aircraft do not drop out of the sky. Smoking made the slow progress from evidence of harm, through taxes and warnings to full-scale bans, and millions of lives were saved.Experimental methods have been used for many decades.

In the kingdom of ends everything has either a price or a dignity. What has a price can be replaced by something else as its equivalent; what on the other hand is raised above all price and therefore admits of no equivalent has a dignity.One of the fascinating features of the history of science is how often new ways of seeing preceded new insights. The achromatic-lens microscope in the early 19th century paved the way for germ theory, and X-ray crystallography in the early 20th century played a vital role in the later discovery of the structure of DNA. In the same way flows of data – for example, about how people move around a city, or how blood cells change – can prompt new insights.But how important is measurement to social change? Many people are attracted to metrics and indices of all kinds. But, as my colleague Mark Moore used to warn, ‘do you really think the leaders of the Civil Rights Movement were counting the placards or measuring the decibels of their cries for human rights?’ In social change, as in our own daily lives, measurement often feels inappropriate for the things that matter most.This chapter examines some of the history of social observation as a tool for public policy, social innovation and social change, and I suggest where it might lead in the future. Without some means of measurement, it can be hard to know if a social innovation is good. It may feel good to the beneficiaries – but still be less effective than an alternative. Or it may work well for one group but not another. And, even if it may not be appropriate to measure the passions of movements, once these ideas become part of the mainstream, and are transformed into the cool logic of laws, regulations and programmes, measurements do start to matter a lot, as the Civil Rights Movement discovered.A short history of measurementFor centuries, governments have sought to map and measure social phenomena in order to better exercise control over them. In the modern era these attempts can be traced back to figures like Sir William Petty in England and the cameralists in Prussia.

This chapter shows that there is a possibility of fostering an enabling and innovative multistakeholder partnership for creating sustainable impact and transformative change with local communities. It argues that the collaborative efforts among district administration, educational institutions and civil society groups in supporting innovation and entrepreneurship can play an extremely important role in livelihood security and empowerment of marginalized sections. The chapter outlines the transformation of a marginalized and underdeveloped district of India. It presents a background of the district with a focus on farmers’ distress and discusses the mode of organization of elites and marginalized peoples under welfare and neoliberal regimes. The chapter also outlines the impact that state–university engagement on the communities. The neoliberal regime made the elite-based cooperatives ineffective, as they came under mismanagement and overexploitation by those in power. Neoliberal reform introduced a new vulnerability among Indian farmers, especially in certain states, such as Maharashtra.

The world is witnessing an unprecedented disruption due to the COVID-19 pandemic in almost all spheres of socio-economic activity. This black swan moment is unprecedented since the advent of the Industrial Revolution. Simultaneously, there is a shift towards effective use of data and technology. There is an exponential increase in the quantum of data collected and has subsequently necessitated a paradigm shift in the functioning of Industry 4.0. Data intelligence, data science, artificial intelligence, machine learning, and deep assimilation of nuanced knowledge revolutionize business and society worldwide. This heralds a potential transition towards data-intensive economies, governments, industrial and social sectors. The accelerated pace of data processing, data intelligence, and analytics encompasses business intelligence, data sciences and machine learning. The spectrum of such upheavals and associated technological transitions is a watershed moment and impacts business and social transformations. In the paper on the role of data in the social realm (Technology as a catalyst for sustainable social business: Advancing the research agenda, 2019). Ashraf et al. opine that “Despite its immense potentials as a sustainable and innovative means to solve specific social problems, the basic concept of the social business model remains unclear to many”. In recent times there has been an inconsistent approach towards social business research. Subsequently, the contemporary business scenario is yet to optimally capitalize on the advantages of the Social Business concept and address the divergent socio-economic and ecological issues worldwide, with profits intact. This should in no way dilute profit maximization for optimizing socio-economic benefits for value creation and sustainability. “Although the social enterprise is often considered to have positive future potential, it is currently underdeveloped” (Bell, 2003). Therefore, social entrepreneurship should generate and ensure nuanced and effective innovations, addressing underserved needs. In contemporary times, tools harnessing Big Data are becoming widely applied, leading to a huge reservoir of untapped diverse data. This subsequently creates an immense opportunity to accelerate the use of Big Data towards social good and sustainability. Though this is a recent trend, it indeed holds promise in the post-COVID world that would be privy to unprecedented socio-economic upheavals and an increased need to address issues of humankind for greater global welfare. In the recent past, diverse data sets have created large-scale solutions in diverse spheres ranging from weather forecasts to airline tickets. Insightful correlations and Big Data go hand in hand and hold the key to several complicated pressing social issues. Therefore, a new crop of social entrepreneurs in public health, social welfare, and humanitarian relief would surely emerge by default. Therefore, it is all the more relevant to make sense of a deluge of Big Data towards alleviating a disease, ecological imbalance, war, and most importantly, the patterns to cope with the disease and its aftermath. This chapter proposes anticipating and predicting the immense possibilities of optimizing Big Data and digitization as key critical drivers of empirical simulation and troubleshooting. Good governance, inclusive society, elimination of corruption, and streamlining policy measures would emerge as default collectives of such social entrepreneurial ventures. The chapter would draw inferences from such models of socially inclined data analytics by data scientists, leading to relevant social models of significance. Implications of the chapter would be to assuage the fault lines, draw inferences from the past, and delve into the plausibility and relevance of Big Data to replicate and innovate socially relevant models to map bigger social issues. This, of course, should have embedded benchmarks of equality, ethics, and empowerment while processing Big Data for a greater social good in a post-COVID world gaping at us.

Social entrepreneurship is today an imperative across the world. The scarcity of resources, the needs of the growing population, and the growing burden on the environment have made people realize a need for organizations that can be profitable while bringing positive change in society and the environment. Sustainable, out-of-the-box solutions are required for solving social challenges. The onus of providing these solutions are often taken by aspiring entrepreneurs, who see opportunity in the challenges and are willing to take risks to create innovative and effective solutions for society’s benefit. The chapter elucidates the meaning of social entrepreneurship, the difference between commercial and social entrepreneurship, the models of social enterprises in practice, and the recommended methodology to evaluate social impact. The chapter features international case studies (through secondary research) and cases about social entrepreneurs who have dramatically improved the lives of people while being financially sustainable – an organization like SELCO Solar Lights Private Ltd, ARMAAN, Yellow Leaf and Arvind Eye Hospital, and many others have provided solutions for the economically weaker section of society. Many of these new-age social entrepreneurs use Big Data and artificial intelligence to ensure that their initiatives create greater social change. Some of these initiatives are highlighted in the chapter.

This chapter examines the close relationships between philanthropy and innovation. A case study of philanthropy improving eyesight in Africa is provided. The crucial impact of philanthropy on science and universities is discussed. A case study of how philanthropy fundamentally changed Queensland University is provided. The intimate relationship between philanthropy and the arts is explored. A case study is provided of the impact of philanthropy on a major arts institution. The connections between philanthropy and social and humanitarian innovation is described.

“Many suppliers and users of social research are dissatisfied, the former because they are not listened to, the latter because they do not hear much they want to listen to” (Lindblom & Cohen, 1979: 1). As mentioned in Chapter 1 of this book, the recent events of global financial crisis and a series of Occupy Wall Street protests have raised reflections of business management education on MBA trainings. The question of whether management research and education can be a facilitator toward events that would eventually destroy the world economy or a facilitator toward achieving social value and human glory has been raised with those reflections. MBA graduates should not be used as profiting tools for big corporations anymore. Moreover, the separation between management theory and industry practice has, for a long time, caused a dilemma with regard to the difficulties inherent in dialogue between academia and industry. There have been urgent calls to embody management research into applicable industry knowledge in order to minimize the gap in between the two (Anderson, Herriot, & Hodgkinson, 2001; Rynes, Bartunek, & Daft, 2001; Van de Yen & Johnson, 2006).