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Addressing a largely underexplored research field, this article centres on the development of indicators to grasp social innovation at different analytical levels: organisational innovativeness, regional innovation capacity, and resonance, to position social innovation in the broader field of innovation.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Notre présentation porte sur la relation partenariale qui prend forme entre des praticiens et des chercheurs dans le cadre de recherches impliquant une relation étroite entre ces deux acteurs. Dans la littérature, ce type de recherche se retrouve sous des dénominations différentes : recherche collaborative, recherche-action, recherche partenariale, recherche participative. Ces dénominations impliquent une relation étroite entre chercheurs et praticiens tout au long du processus de recherche. Cette collaboration est concrétisée par le terme de coconstruction des connaissances dont se réclament ces différentes appellations. Nous postulons que cet espace de production cognitive repose sur un dialogue

La relation entre l’innovation sociale et la gouvernance n’est pas suffisamment explorée dans la littérature scientifique. Les analyses que l’on trouve sur ce sujet se rapportent principalement à la relation entre l’administration publique, la participation citoyenne, le changement social et les nouvelles formes de gouvernance (Lévesque, 2012 ; Moulaert<em>et al</em>., 2007 ; Novy, Hamme et Leubolt, 2009). Dans ces approches, l’innovation sociale est abordée comme le rapport entre les relations sociales et la gouvernance. Ainsi, cette perspective cherche plutôt à comprendre comment certains groupes sociaux développent leurs capacités sociopolitiques pour garantir leur accès aux ressources qui permettent de répondre

La relation entre l’innovation sociale et la gouvernance n’est pas suffisamment explorée dans la littérature scientifique. Les analyses que l’on trouve sur ce sujet se rapportent principalement à la relation entre l’administration publique, la participation citoyenne, le changement social et les nouvelles formes de gouvernance (Lévesque, 2012 ; Moulaert<em>et al</em>., 2007 ; Novy, Hamme et Leubolt, 2009). Dans ces approches, l’innovation sociale est abordée comme le rapport entre les relations sociales et la gouvernance. Ainsi, cette perspective cherche plutôt à comprendre comment certains groupes sociaux développent leurs capacités sociopolitiques pour garantir leur accès aux ressources qui permettent de répondre