Votre recherche

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.

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 innovations have proven to be valuable in identifying, designing and implementing new solutions to social and environmental problems. The recent COVID-19 outbreak has put a spotlight on the potential of social innovation as a resilience mechanism, including for local development. This paper presents a preliminary framework for analysing social innovation ecosystems at the local level. It can help policy makers to better understand the different concepts around social innovation, and to develop policies to support social innovation and its implementation. The first section considers the features of social innovation and the benefits it can bring. The second section provides an analytical framework for social innovation at the local level. The final section sets a number of guidelines that support the implementation of social innovation ecosystems at local level, including examples of specific policy instruments.

Social innovations have proven to be valuable in identifying, designing and implementing new solutions to social and environmental problems. The recent COVID-19 outbreak has put a spotlight on the potential of social innovation as a resilience mechanism, including for local development. This paper presents a preliminary framework for analysing social innovation ecosystems at the local level. It can help policy makers to better understand the different concepts around social innovation, and to develop policies to support social innovation and its implementation. The first section considers the features of social innovation and the benefits it can bring. The second section provides an analytical framework for social innovation at the local level. The final section sets a number of guidelines that support the implementation of social innovation ecosystems at local level, including examples of specific policy instruments.

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.

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.

Interest in social innovations (SIs) from both the academic and the policy side is growing. Nonetheless, we still know little about which sustainable development goals (SDGs) SIs already address. Furthermore, only little is known about who the innovators developing and implementing SIs are. In this paper, we aim to bring more clarity and structure to the field of SIs. Firstly, a systematic literature review was conducted, before a content analysis was used to analyze the definitions used with regard to similarities. Secondly, all case studies described in the reviewed articles were then further systematically analyzed in order to identify the social or environmental problems addressed and the innovators involved. For the purpose of classifying the diverse types of problems, we used the globally known and broadly accepted 17 sustainable development goals (SDGs). Results showed that most SI case studies deal with an improvement of health and well-being. Furthermore, our study illustrates that there is a pronounced difference in the focus of SIs between developing and developed countries. Concerning the innovators, our results indicate that five types of innovators are fundamentally involved in developing and implementing SIs: social entrepreneurs, NGOs and non-profits, public institutions, civil society, firms, and social enterprises. Our definition analysis as well as the identification and classification of the innovators and addressed social needs bring much-needed clarity and structure to the field. However, our systematic review shows that SI is still in its infancy and it will be interesting to see where the field will head.

Abstract All over the world there are millions of social entrepreneurs that come up with potential social innovations. Some never get implemented in practice. Others are implemented, but then the passion fades or the solution does not reveal itself as promising for creating social impact. In some cases, the lack of sustainability or management capacity prevents a successful scaling up process. Despite all these potential obstacles, there are social innovations that go from promising ideas to becoming mainstream solutions, leading to new markets, industries, or social movements, such as Microfinance or Wikipedia. An in-depth look at main obstacles facing social innovators and the leadership skills required to overcome them is a meaningful contribution to the field of social innovation. The goal of this chapter is to propose such a contribution through an in-depth exploration of the life cycle of social innovation. The term “life cycle” implies a sequence of stages in the evolution of new ventures (Parker 2007).