Understanding Social Entrepreneurship in the African Context: An Exploratory Review of Evidence From Nigeria

Social enterprises are organizations created with the aim of applying entrepreneurial skills and innovations to solving social problems. They are managed by individuals who combine pragmatic and result-oriented methods of a business entrepreneur with the goals of a social reformer. Such enterprises combine resources in innovative ways to create social value in and for the society. However, social enterprises may face challenges that impact their ability to accomplish social goals. For instance, when confronted with the harsh realities of economic recession, teaming poor population, and the need to profit for social intervention, social enterprises existing in hostile economic environment in developing countries may face possibilities of shutdown. This chapter examines the concept of social entrepreneurship in a subsisting economy in Africa. Specifically, it draws from relevant primary and secondary data to explore the nature of social entrepreneurship in the Nigeria context and the potential role that social entrepreneurship can play in addressing social problems

Uncertainty in the Economics of Climate Change: Can Global Sensitivity Analysis be of Help?

The complexity of integrated assessment models (IAMs) prevents the direct appreciation of the impact of uncertainty on the model predictions. However, for a full understanding and corroboration of model results, analysts might be willing, and ought to identify the model inputs that influence the model results the most (key drivers), appraise the relevance of interactions and the direction of change associated with the simultaneous variation of the model inputs. We show that such information is already contained in the data set produced by Monte Carlo simulations and that it can be extracted without additional calculations. Our discussion is guided by an application of the proposed methodologies to the well-known DICE model of William Nordhaus (2008). A comparison of the proposed methodology to approaches previously applied on the same model shows that robust insights concerning the dependence of future atmospheric temperature, global emissions and current carbon costs and taxes on the model’s exogenous inputs can be obtained. The method avoids the fallacy of a priori deeming the important factors based on sole intuition

Uncertainty in the Economics of Climate Change: Can Global Sensitivity Analysis be of Help?

The complexity of integrated assessment models (IAMs) prevents the direct appreciation of the impact of uncertainty on the model predictions. However, for a full understanding and corroboration of model results, analysts might be willing, and ought to identify the model inputs that influence the model results the most (key drivers), appraise the relevance of interactions and the direction of change associated with the simultaneous variation of the model inputs. We show that such information is already contained in the data set produced by Monte Carlo simulations and that it can be extracted without additional calculations. Our discussion is guided by an application of the proposed methodologies to the well-known DICE model of William Nordhaus (2008). A comparison of the proposed methodology to approaches previously applied on the same model shows that robust insights concerning the dependence of future atmospheric temperature, global emissions and current carbon costs and taxes on the model’s exogenous inputs can be obtained. The method avoids the fallacy of a priori deeming the important factors based on sole intuition

Uncertainty in the Economics of Climate Change: Can Global Sensitivity Analysis be of Help?

The complexity of integrated assessment models (IAMs) prevents the direct appreciation of the impact of uncertainty on the model predictions. However, for a full understanding and corroboration of model results, analysts might be willing, and ought to identify the model inputs that influence the model results the most (key drivers), appraise the relevance of interactions and the direction of change associated with the simultaneous variation of the model inputs. We show that such information is already contained in the data set produced by Monte Carlo simulations and that it can be extracted without additional calculations. Our discussion is guided by an application of the proposed methodologies to the well-known DICE model of William Nordhaus (2008). A comparison of the proposed methodology to approaches previously applied on the same model shows that robust insights concerning the dependence of future atmospheric temperature, global emissions and current carbon costs and taxes on the model’s exogenous inputs can be obtained. The method avoids the fallacy of a priori deeming the important factors based on sole intuition

Justifying the ROI of social media investment in education

To invest or not to invest? In the context of Higher Education, the decision around social media adoption is ultimately driven by the end users—students—increasingly demanding in their expectations of technology support provided by universities. This presents a new set of challenges to HE institutions of how to effectively adopt social media in a range of modes provided to students, alumni, external stakeholders, etc. This chapter sets the agenda for future research into methods of measuring effectiveness of social media applications in Higher Education. Drawing on a rich account of social media applications throughout the entire student lifecycle, the chapter identifies common objectives to social media campaigns and uses in educational settings. A framework for social strategy adoption by HE institutions is proposed for further empirical testing. The chapter provides an approach to measuring the effectiveness of social media in higher education and offers practical recommendations and identifies areas needing future research