Votre recherche

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.

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.

The idea of social innovation has become increasingly popular in recent years, and as often happens with popular concepts it risks become overloaded. This happens, for instance, when social innovation is seen as the “soft” and humanistic alternative to versions of innovation dominated by science and technology. There has been a fast growing literature on social innovation, some of it in academic publications but perhaps most of it published by think tanks, semigovernmental agencies, and other organizations. Many different institutional fields for social innovation are discussed in the literature (Moulaert et al. 2013). Education is one of them, but much less frequently treated than fields like housing, intercultural relations, environmental issues, and childcare.

The idea of social innovation has become increasingly popular in recent years, and as often happens with popular concepts it risks become overloaded. This happens, for instance, when social innovation is seen as the “soft” and humanistic alternative to versions of innovation dominated by science and technology. There has been a fast growing literature on social innovation, some of it in academic publications but perhaps most of it published by think tanks, semigovernmental agencies, and other organizations. Many different institutional fields for social innovation are discussed in the literature (Moulaert et al. 2013). Education is one of them, but much less frequently treated than fields like housing, intercultural relations, environmental issues, and childcare.

The socioeconomic challenges caused by aging populations have encouraged many countries to reevaluate the place of the elderly in society as well as to adopt measures in encouraging them to be participative. In recent decades, crowdsourcing has been identified as a rapid growth of innovative Internet-based information and communication technologies in giving the opportunities to educational organizations to reach their goals. With their accumulated skills and knowledge, academic retirees can be resourceful to society. However, their knowledge and experiences seem to be undervalued and underutilized. Retired academics have better opportunity to extend their contribution in the society as their valuable knowledge is more appreciated than people from other background. Retired academics tend to be able to fulfill their desire for professional continuity following retirement more markedly than people from other backgrounds. This paper analyzes the use of crowdsourcing in educational activities, especially for the academic retirees. Therefore, the objectives of this paper are to take an exploratory look on how educational organizations use crowdsourcing as part of their activities at the present time, and to suggest how the practice of crowdsourcing may expand to other educational activities in future.

The socioeconomic challenges caused by aging populations have encouraged many countries to reevaluate the place of the elderly in society as well as to adopt measures in encouraging them to be participative. In recent decades, crowdsourcing has been identified as a rapid growth of innovative Internet-based information and communication technologies in giving the opportunities to educational organizations to reach their goals. With their accumulated skills and knowledge, academic retirees can be resourceful to society. However, their knowledge and experiences seem to be undervalued and underutilized. Retired academics have better opportunity to extend their contribution in the society as their valuable knowledge is more appreciated than people from other background. Retired academics tend to be able to fulfill their desire for professional continuity following retirement more markedly than people from other backgrounds. This paper analyzes the use of crowdsourcing in educational activities, especially for the academic retirees. Therefore, the objectives of this paper are to take an exploratory look on how educational organizations use crowdsourcing as part of their activities at the present time, and to suggest how the practice of crowdsourcing may expand to other educational activities in future.

The aim of this research is to explore the dynamics and impact of open social innovation, within the context of fab labs and makerspaces. Using an exploratory methodology based on 12 semi-structured interviews of fab lab founders belonging to The Centres for Maker Innovation and Technology (CMIT) programme – a network of 170 fab labs located in Eastern Europe – this research explores the impact of an adopting an open approach in relation to the different stages of social innovation (prompts, proposals, prototypes, sustaining, scaling and diffusion, systemic change) as well as social impact. The main results of this study are that while the CMIT programme provided each fab lab with similar initial conditions (identical funding, objectives and rules), the open social innovation approached adopted enabled to give birth to a wide diversity of fab labs, each being very well adapted to the local environment, social needs and constraints and able to deliver social impact in just a matter of years; a result that would be hard to achieve with a centralised top-down approach. The study identified three types of CMITs – Education, Industry and Residential – which could be similar or different depending on the stage of social open innovation. Furthermore, this paper discusses the main difficulties social entrepreneurs encounter as a part of the open social innovation process, as well as means to overcome them. In this respect, this study adds to the literature on fab labs by providing more comprehensive view of the challenges faced by fab labs (and makerspaces) founders, as well as suggestions of strategies enabling to ensure their long-term sustainability.

The aim of this research is to explore the dynamics and impact of open social innovation, within the context of fab labs and makerspaces. Using an exploratory methodology based on 12 semi-structured interviews of fab lab founders belonging to The Centres for Maker Innovation and Technology (CMIT) programme – a network of 170 fab labs located in Eastern Europe – this research explores the impact of an adopting an open approach in relation to the different stages of social innovation (prompts, proposals, prototypes, sustaining, scaling and diffusion, systemic change) as well as social impact. The main results of this study are that while the CMIT programme provided each fab lab with similar initial conditions (identical funding, objectives and rules), the open social innovation approached adopted enabled to give birth to a wide diversity of fab labs, each being very well adapted to the local environment, social needs and constraints and able to deliver social impact in just a matter of years; a result that would be hard to achieve with a centralised top-down approach. The study identified three types of CMITs – Education, Industry and Residential – which could be similar or different depending on the stage of social open innovation. Furthermore, this paper discusses the main difficulties social entrepreneurs encounter as a part of the open social innovation process, as well as means to overcome them. In this respect, this study adds to the literature on fab labs by providing more comprehensive view of the challenges faced by fab labs (and makerspaces) founders, as well as suggestions of strategies enabling to ensure their long-term sustainability.

- Features interdisciplinary expertise from economics, law, technology and social science on the practice of co-creation - Provides best-practices and management approaches to successful co-creation - Enables research-based and practice-relevant understanding of the background and concepts around co-creation

- Features interdisciplinary expertise from economics, law, technology and social science on the practice of co-creation - Provides best-practices and management approaches to successful co-creation - Enables research-based and practice-relevant understanding of the background and concepts around co-creation