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La Société du Quartier de l’innovation de Montréal est un Organisme à but non lucratif (OBNL) qui a été créé en 2013 à l’initiative de l’Université McGill et de l’École de technologie supérieure (ÉTS). NOTRE MISSION Cultiver un écosystème d’innovation unique au cœur de Montréal et favoriser la collaboration et l’expérimentation entre les milieux académique, entrepreneurial et citoyen dans le but de créer des retombées positives pour la société. Notre connaissance du milieu de l’innovation québécois est exhaustive

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 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.

University students will be our future business leaders, and will have to address social problems caused by business by implementing solutions such as social entrepreneurship ventures. In order to facilitate the learning process that will foster social entrepreneurship, however, a more holistic pedagogy is needed. Based on learning theory, we propose that students' social entrepreneurship actions will depend on their learning about CSR and their absorptive capacity. We propose that instructors and higher education institutions can enhance this absorptive capacity by exploiting Web 2.0 technologies. We tested our proposition with a sample of 425 university students using structural equation modeling and found support for the proposed relationships.

University students will be our future business leaders, and will have to address social problems caused by business by implementing solutions such as social entrepreneurship ventures. In order to facilitate the learning process that will foster social entrepreneurship, however, a more holistic pedagogy is needed. Based on learning theory, we propose that students' social entrepreneurship actions will depend on their learning about CSR and their absorptive capacity. We propose that instructors and higher education institutions can enhance this absorptive capacity by exploiting Web 2.0 technologies. We tested our proposition with a sample of 425 university students using structural equation modeling and found support for the proposed relationships.

The main purpose of this article is to introduce the Social Enterprise Model Canvas (SEMC), a Business Model Canvas (BMC) conceived for designing the organizational settings of social enterprises, for resolving the mission measurement paradox, and for meeting the strategy, legitimacy and governance challenges. The SEMC and the analysis that explains its features are of interest to academics concerned with the study of social entrepreneurship because they offer a new analytical tool that is particularly useful for untangling and comparing different forms of social enterprises. Also, it is of interest to social entrepreneurs, because the SEMC is a platform that can be used to prevent 'mission drifts' that might result from problems emerging from the mismanagement of such challenges. The arguments presented are grounded on scientific literature from multiple disciplines and fields, on a critical review of the BMC, and on a case study. The main features of SEMC that makes it an alternative to the BMC are attention to social value and building blocks that take into consideration non-targeted stakeholders, principles of governance, the involvement of customers and targeted beneficiaries, mission values, short-term objectives, impact and output measures.

The main purpose of this article is to introduce the Social Enterprise Model Canvas (SEMC), a Business Model Canvas (BMC) conceived for designing the organizational settings of social enterprises, for resolving the mission measurement paradox, and for meeting the strategy, legitimacy and governance challenges. The SEMC and the analysis that explains its features are of interest to academics concerned with the study of social entrepreneurship because they offer a new analytical tool that is particularly useful for untangling and comparing different forms of social enterprises. Also, it is of interest to social entrepreneurs, because the SEMC is a platform that can be used to prevent 'mission drifts' that might result from problems emerging from the mismanagement of such challenges. The arguments presented are grounded on scientific literature from multiple disciplines and fields, on a critical review of the BMC, and on a case study. The main features of SEMC that makes it an alternative to the BMC are attention to social value and building blocks that take into consideration non-targeted stakeholders, principles of governance, the involvement of customers and targeted beneficiaries, mission values, short-term objectives, impact and output measures.

Interrelations between creativity, innovativeness and entrepreneurial skills of individuals have long been discussed in the literature. Due to the challenges regarding their measurement, most studies focused on the intentions rather than the outcomes. The idea generation that requires creativity is the first stage of social innovation. The young population's creative potentials in participating social innovation practices deserve a special attention as they play a critical role in the innovativeness and entrepreneurship of societies. This study aims to explore the factors that determine the creative intentions of university students that are important in generating social innovation projects. A structured survey based on the literature was conducted among 600 management and engineering students from 3 universities from the different percentiles of the Entrepreneurial and Innovative University Index for 2012 of the Turkish Ministry of Science, Industry and Technology. The survey included questions on the demographic characteristics, environmental factors, motivators, university/institutional context, perceptions and creative thinking attitudes. By conducting reliability and factor analysis, accuracy and validity of data is tested and the impact factors were identified. Findings reveal that visionary attitude, curiosity, exploration and learning, attitude for own creativity, self-esteem, perception about the learnability of creativity, university and social environment are components of creative thinking intentions of students and some of these factors vary by year of study and university.

Interrelations between creativity, innovativeness and entrepreneurial skills of individuals have long been discussed in the literature. Due to the challenges regarding their measurement, most studies focused on the intentions rather than the outcomes. The idea generation that requires creativity is the first stage of social innovation. The young population's creative potentials in participating social innovation practices deserve a special attention as they play a critical role in the innovativeness and entrepreneurship of societies. This study aims to explore the factors that determine the creative intentions of university students that are important in generating social innovation projects. A structured survey based on the literature was conducted among 600 management and engineering students from 3 universities from the different percentiles of the Entrepreneurial and Innovative University Index for 2012 of the Turkish Ministry of Science, Industry and Technology. The survey included questions on the demographic characteristics, environmental factors, motivators, university/institutional context, perceptions and creative thinking attitudes. By conducting reliability and factor analysis, accuracy and validity of data is tested and the impact factors were identified. Findings reveal that visionary attitude, curiosity, exploration and learning, attitude for own creativity, self-esteem, perception about the learnability of creativity, university and social environment are components of creative thinking intentions of students and some of these factors vary by year of study and university.

Eleven papers explore research in entrepreneurship and community engagement in the context of Syracuse University's Scholarship in Action Model, which emphasizes sustainable campus-community entrepreneurial partnerships and applied research on the outcomes of these. Papers discuss the five keys to success in academic entrepreneurship; transforming a professional curriculum through engagement with practice--the Global Enterprise Technology Program at Syracuse University; tapping our fountain of youth--the guiding philosophy and first report on the Syracuse Student Startup Accelerator; Syracuse University Technology Commercialization Clinics; community development law and legal education; the Syracuse Miracle--inspiring entrepreneurs through conversations; the South Side Newspaper Project; bridging a traumatic past to an envisioned future--a case study of social entrepreneurship; inclusive entrepreneurship; the role of information and motivation in the process of innovation; and students serving as catalysts within a teacher education innovation.

La Société du Quartier de l’innovation de Montréal est un Organisme à but non lucratif (OBNL) qui a été créé en 2013 à l’initiative de l’Université McGill et de l’École de technologie supérieure (ÉTS). NOTRE MISSION Cultiver un écosystème d’innovation unique au cœur de Montréal et favoriser la collaboration et l’expérimentation entre les milieux académique, entrepreneurial et citoyen dans le but de créer des retombées positives pour la société. Notre connaissance du milieu de l’innovation québécois est exhaustive