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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 21st century has brought a cornucopia of new knowledge and technologies. But there has been little progress in our ability to solve social problems using social innovation – the deliberate invention of new solutions to meet social needs - across the globe. Geoff Mulgan is a pioneer in the global field of social innovation. Building on his experience advising international governments, businesses and foundations, he explains how it provides answers to today’s global social, economic and sustainability issues. He argues for matching R&D in technology and science with a socially focused R&D and harnessing creative imagination on a larger scale than ever before. Weaving together history, ideas, policy and practice, he shows how social innovation is now coming of age, offering a comprehensive view of what can be done to solve the global social challenges we face.

The 21st century has brought a cornucopia of new knowledge and technologies. But there has been little progress in our ability to solve social problems using social innovation – the deliberate invention of new solutions to meet social needs - across the globe. Geoff Mulgan is a pioneer in the global field of social innovation. Building on his experience advising international governments, businesses and foundations, he explains how it provides answers to today’s global social, economic and sustainability issues. He argues for matching R&D in technology and science with a socially focused R&D and harnessing creative imagination on a larger scale than ever before. Weaving together history, ideas, policy and practice, he shows how social innovation is now coming of age, offering a comprehensive view of what can be done to solve the global social challenges we face.

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.

Understanding the criteria for the formation and development of social innovation ecosystems is crucial to establish appropriate strategies for their creation, maintenance and expansion. In this regard, strategies should be focused on social development actions, mainly supported by governments and members of the society. Silva, Sá and Spinosa (2019) reinforce that the interaction between government, industry and academia, coined in the literature as Triple Helix, by Etzkowitz and Leydesdorff (2000), has been increasingly recognized for driving the transformation of scientific and technological results into economic results. According to a study by Schaffers et. al (2012), the progress towards the understanding of the intersection between urban economy, innovation networks, technology platforms, services and their applications, collective intelligence and innovation theories themselves is one of the challenges for innovation. This understanding can help scholars, governments and professionals to explore new directions and produce knowledge and solutions to make cities smarter. This study aimed to carry on a previous study by Nespolo and Fachinelli (2017) as well as build and validate a scale to measure the perception of social innovation ecosystems.

Understanding the criteria for the formation and development of social innovation ecosystems is crucial to establish appropriate strategies for their creation, maintenance and expansion. In this regard, strategies should be focused on social development actions, mainly supported by governments and members of the society. Silva, Sá and Spinosa (2019) reinforce that the interaction between government, industry and academia, coined in the literature as Triple Helix, by Etzkowitz and Leydesdorff (2000), has been increasingly recognized for driving the transformation of scientific and technological results into economic results. According to a study by Schaffers et. al (2012), the progress towards the understanding of the intersection between urban economy, innovation networks, technology platforms, services and their applications, collective intelligence and innovation theories themselves is one of the challenges for innovation. This understanding can help scholars, governments and professionals to explore new directions and produce knowledge and solutions to make cities smarter. This study aimed to carry on a previous study by Nespolo and Fachinelli (2017) as well as build and validate a scale to measure the perception of social innovation ecosystems.

Qu'est-ce que l'innovation et comment s'y prendre pour la mesurer ? Comprendre l'échelle des activités d'innovation, les caractéristiques des entreprises innovantes, ainsi que les facteurs internes et systémiques en jeu est une condition préalable essentielle à la mise en œuvre et l'analyse des politiques destinées à stimuler l'innovation. Paru pour la première fois en 1992, le Manuel d'Oslo s'est imposé comme une référence internationale pour la collecte et l'utilisation des données sur l'innovation. Pour cette quatrième édition, le manuel a été étoffé afin de couvrir un éventail plus large de phénomènes liés à l'innovation et de tenir compte de l'expérience acquise au fil des cycles récents des enquêtes connexes réalisées dans les pays de l'OCDE, dans les économies partenaires et par d'autres organisations.

Qu'est-ce que l'innovation et comment s'y prendre pour la mesurer ? Comprendre l'échelle des activités d'innovation, les caractéristiques des entreprises innovantes, ainsi que les facteurs internes et systémiques en jeu est une condition préalable essentielle à la mise en œuvre et l'analyse des politiques destinées à stimuler l'innovation. Paru pour la première fois en 1992, le Manuel d'Oslo s'est imposé comme une référence internationale pour la collecte et l'utilisation des données sur l'innovation. Pour cette quatrième édition, le manuel a été étoffé afin de couvrir un éventail plus large de phénomènes liés à l'innovation et de tenir compte de l'expérience acquise au fil des cycles récents des enquêtes connexes réalisées dans les pays de l'OCDE, dans les économies partenaires et par d'autres organisations.

The innovation journey is a process model distinguishing between the initiation, developmental and implementation/termination period of innovations; it looks at drivers and barriers, like innovation managers, investors, setbacks, adaptation, infrastructure. We operationalize this model to apply it to the process of social innovation. Eighty-two cases are re-analysed in a secondary analysis using qualitative comparative analysis to assess how social innovations develop and to investigate if they resemble the ‘innovation journey’ of innovations in technology/business.

In the business ethics literature, the growing interest in social entrepreneurship has remained limited to the assumption that pursuing a social mission will clash against the pursuit of associated economic achievements. This ignores recent developments in the social entrepreneurship literature which show that social missions and economic achievement can also have a mutually constitutive relation. We address this gap adopting the notion of shared value (SV) for an ethical inquiry of social entrepreneurship. Using a sensemaking framework, we assume that the emergence of SV propositions can be captured through the analysis of how social entrepreneurs make sense of events of change, selecting the journey of three exemplar cases for an inductive empirical inquiry. From our findings, we propose three themes for further examination. First, the ethical groundings of entrepreneurial SV are mostly shaped by idiosyncratic imperatives that inform both social mission and economic gain from the onset. Second, the ethical groundings of entrepreneurial SV will be likely operationalised as a filtering device, which allows for resilience as well as potentially detrimental blind spots. And third, the ethical groundings of entrepreneurial SV are expressed through ongoing transparency. Whilst there are agendas, these are not necessarily hidden but instead are likely put on show for the scrutiny of markets and communities. We hope that this evidence can add more light to our still modest understanding of the ethical groundings of social entrepreneurship.

In the business ethics literature, the growing interest in social entrepreneurship has remained limited to the assumption that pursuing a social mission will clash against the pursuit of associated economic achievements. This ignores recent developments in the social entrepreneurship literature which show that social missions and economic achievement can also have a mutually constitutive relation. We address this gap adopting the notion of shared value (SV) for an ethical inquiry of social entrepreneurship. Using a sensemaking framework, we assume that the emergence of SV propositions can be captured through the analysis of how social entrepreneurs make sense of events of change, selecting the journey of three exemplar cases for an inductive empirical inquiry. From our findings, we propose three themes for further examination. First, the ethical groundings of entrepreneurial SV are mostly shaped by idiosyncratic imperatives that inform both social mission and economic gain from the onset. Second, the ethical groundings of entrepreneurial SV will be likely operationalised as a filtering device, which allows for resilience as well as potentially detrimental blind spots. And third, the ethical groundings of entrepreneurial SV are expressed through ongoing transparency. Whilst there are agendas, these are not necessarily hidden but instead are likely put on show for the scrutiny of markets and communities. We hope that this evidence can add more light to our still modest understanding of the ethical groundings of social entrepreneurship.

This article uses a conceptual approach to propose an innovation model for regional universities. It demonstrates that the traditional university encounters several obstacles that hinder its full integration into the development of its respective region and explains why currently known models cannot adapt to regions that have deficient relationships with the government and lack an entrepreneurial base. The new model is based on a structure composed of units called “innovation hubs” and incorporates social innovation, thus permitting the university to become integrated into the regional innovation ecosystems. The Magdalena University in Colombia was used as a reference in developing the model. Keywords: hub; social innovation; university innovation models; regional innovation ecosystems

This article uses a conceptual approach to propose an innovation model for regional universities. It demonstrates that the traditional university encounters several obstacles that hinder its full integration into the development of its respective region and explains why currently known models cannot adapt to regions that have deficient relationships with the government and lack an entrepreneurial base. The new model is based on a structure composed of units called “innovation hubs” and incorporates social innovation, thus permitting the university to become integrated into the regional innovation ecosystems. The Magdalena University in Colombia was used as a reference in developing the model. Keywords: hub; social innovation; university innovation models; regional innovation ecosystems

Ten papers consider new sources of entrepreneurial finance, highlighting angel investors, government interventions, financial technology innovations, and how entrepreneurs' characteristics relate to fundraising success. Papers discuss the role of angel syndicates on the demand and supply of informal venture capital (VC); government intervention in VC markets; the validity of guarantee instruments; green project crowdfunding; linguistic style approaches for gaining empathetic attention from crowdfunding investors; blockchain, cryptocurrency, and initial coin offerings; the development of the minibond market for small and medium-sized enterprises; facilitating access to early-stage equity financing in developing countries; the financial literacy of entrepreneurs; and the pedagogical value of social entrepreneurship competitions at the individual level. Quas is Senior Researcher in Corporate Finance at Universita degli Studi di Milano. Alperovych is Associate Professor of Corporate Finance at EMLYON Business School. Bellavitis is Lecturer of Innovation and Entrepreneurship in the Faculty of Management and International Business of Auckland Business School at the University of Auckland. Paeleman is Assistant Professor at the University of Antwerp. Kamuriwo is Associate Professor in Strategy in the Cass Business School at City, University of London. Index.