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This paper explores the origin of entrepreneurial orientations (EO) from an organizational embeddedness perspective. It examines the impacts of firms’ network embeddedness such as structural, positional and relational on three dimensions of EO, namely, risk-taking, proactiveness and innovativeness. After a brief review of the EO construct and social network theory, we derive a set of testable propositions that relate embeddedness properties such as centrality, structural holes, direct/indirect ties, and network density, to the magnitude of three key EO dimensions. We argue that each dimension may vary independently with each other and has its own formation mechanism, which entails rich implications for entrepreneurial network research.

Goffman (1963) provided us with an explanation of the operation of stigma in microinteractional contexts. However, his definition and explication of the experiences and processes of stigmatization predate what many consider to be the most major shift in discourse and categorization to develop in the twentieth century – the rise of the language of risk. In this chapter, I discuss the intersections of risk discourse and stigma. Drawing on my empirical research with families affected by incarceration, I illustrate the shift toward structural stigma as an exercise of power and governance. I argue that contemporary “common-sense” understandings and usage of the term stigma emphasize negative individual interactions while ignoring the ways that risk categorizations, even in seemingly benign contexts, create structural disadvantage and serve to “other” stigmatized individuals. Singular focus on stigma at the microinteractional level, particularly in destigmatization campaigns, obscures the pervasive structural stigma couched in the language of risk management that permits systematic marginalization.

This chapter proposes a vector autoregressive VAR model with structural shocks (SVAR) that are identified using sign restrictions, and whose distribution is subject to time varying skewness. The authors also present an efficient Bayesian algorithm to estimate the model. The model allows tracking joint asymmetric risks to macroeconomic variables included in the SVAR, and provides a structural narrative to the evolution of those risks. When faced with euro area data, our estimation suggests that there has been a significant variation in the skewness of demand, supply and monetary policy shocks. Such variation can explain a significant proportion of the joint dynamics of real GDP growth and inflation, and also generates important asymmetric tail risks in those macroeconomic variables. Finally, compared to the literature on growth- and inflation-at-risk, the authors find that financial stress indicators are not enough to explain all the macroeconomic tail risks.

Choice under risk has a large stochastic (unpredictable) component. This chapter examines five stochastic models for binary discrete choice under risk and how they combine with “structural” theories of choice under risk. Stochastic models are substantive theoretical hypotheses that are frequently testable in and of themselves, and also identifying restrictions for hypothesis tests, estimation and prediction. Econometric comparisons suggest that for the purpose of prediction (as opposed to explanation), choices of stochastic models may be far more consequential than choices of structures such as expected utility or rank-dependent utility.

Ever since human society developed, environmental and social changes have led to major challenges that must be dealt with. Some of these major challenges are seen as “disasters,” for which a definition that is frequently used is similar to “A serious disruption of the functioning of a community or a society causing widespread human, material, economic or environmental losses which exceed the ability of the affected community or society to cope using its own resources” (UNISDR, 2004; see, e.g., Quarantelli, 1998, and Furedi, 2007, for discussions on the meaning(s) of “disaster”). From witnessing disasters and being forced to work through the aftermath, humanity has been shifting toward trying to reduce disasters’ impacts or to avert them entirely. This field has the modern-day interpretation of “disaster risk reduction,” defined as “The conceptual framework of elements considered with the possibilities to minimize vulnerabilities and disaster risks throughout a society, to avoid (prevention) or to limit (mitigation and preparedness) the adverse impacts of hazards, within the broad context of sustainable development” (UNISDR, 2004).