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Heritability studies attempt to estimate the contribution of genes (vs. environments) to variation in phenotypes (or outcomes of interest) in a given population at a given time. This chapter scrutinizes heritability studies of adverse health phenotypes, emphasizing flaws that have become more glaring in light of recent advances in the life sciences and manifest most visibly in epigenetics.

Drawing on a diverse body of research and critical scholarship, this chapter examines the veracity of methodological and conceptual assumptions of heritability studies.

The chapter argues that heritability studies are futile for two reasons: (1) heritability studies suffer from serious methodological flaws with the overall effect of making estimates inaccurate and likely biased toward inflated heritability, and, more importantly (2) the conceptual (biological) model on which heritability studies depend – that of identifiably separate effects of genes versus the environment on phenotype variance – is unsound. As discussed, contemporary bioscientific work indicates that genes and environments are enmeshed in a complex (bidirectional, interactional), dynamic relationship that defies any attempt to demarcate separate contributions to phenotype variance. Thus, heritability studies attempt the biologically impossible. The emerging research on the importance of microbiota is also discussed, including how the commensal relationship between microbial and human cells further stymies heritability studies.

Understandably, few sociologists have the time or interest to be informed about the methodological and theoretical underpinnings of heritability studies or to keep pace with the incredible advances in genetics and epigenetics over the last several years. The present chapter aims to provide interested scholars with information about heritability and heritability estimates of adverse health outcomes in light of recent advances in the biosciences.

Within the past twenty years, the transition to adulthood has become a burgeoning area of research. The status attainment process, an early model for transition to adulthood research, has given way to research focusing on singular outcomes such as completing formal education, leaving home, obtaining employment, forming a union through marriage or cohabitation, and becoming a parent. As young adults continue to delay family formation, some argue that one’s first experience of heterosexual intercourse is also a symbol of adult status (Meier, 2001). Although most scholars agree that these outcomes along with chronological age symbolize being an adult, relatively few empirical studies examine them as inter-dependent transitions. A recent comparison of these indicators by gender, race, and social class is also needed.

Despite the pendulum swing from utopian to dystopian views of the Internet, the direction of the popular and academic literature continues to lean toward its liberatory potential, particularly as a tool for redressing social inequality. At the same time, decades of digital inequality scholarship have shown persistent socioeconomic inequality in Internet access and use. Yet most of this research captures class by individualized income and education variables, rather than a power relational framework. By tracing research on how fear, control, and risk manifest itself with inequalities related to digital content, digital activism, and digital work, I argue that a narrow stratification approach may miss the full cause and effect of digital inequality. Instead, a class analysis based on power relations may contribute to a broader and more precise theoretical lens to understand the digital divide. As a result, technology can reinforce, or even exacerbate, existing patterns of social and economic inequality because of this power differential.

We empirically investigate the forecasting ability of USD-INR exchange rate volatility models by considering Google Trends data. Within a multiple regression framework, we use historical volatility and liquidity measures to build our benchmark volatility model (Chandra & Thenmozhi, 2014). Moreover, we extend Bulut (2018) to incorporate indexes for 15 keywords (price-related, income-related, and liquidity-related) from Google Trends data into our benchmark volatility model to evaluate the forecasting ability of the models. Our results indicate that Google Trends data can improve volatility prediction and that among the groups of keywords that we consider, the price-related keywords have the best forecasting ability. Incorporating data on searches for “prices” into the model produces the highest reduction in the forecasting error: a 22.75% decrease compared to the level in the benchmark model. Hence, these empirical findings indicate that Google Trends data contain information that influences exchange rate movements.

Communication aids sports teams in achieving physical feats through the process of teamwork. Working in fast-paced, high-pressure environments, sports teams are epitomized by their focus on effective physical and mental coordination, constantly adapting as a team to changing information and dynamic opponents. However, successful physical and mental coordination in sports teams are dependent on communication that may occur well before gameplay. For this reason, coaches and team leaders focus on various communicative activities throughout the life of a sports team, such as knowledge sharing, role clarity, goal setting, motivation, culture, and cohesion. Sports teams also provide unique insights into how teams communicate in single-gender groups, how heightened emotion affects team performance, and how event finality plays a role in team process.

This chapter investigates the geographical variations and approaches that shape the implementation of smart city projects on a global scale. Recognizing the significant influence of contextual factors on smart city initiatives, this chapter aims to comprehend the dynamics that drive the objectives and approaches of smart city projects across different countries. This chapter provides an overview of the strategic and geographical factors that impact smart city implementation, considering organizational drivers and practices, such as the Hofstede model in context. It explores the role of citizen-based, technology-based, and decision-making-based development in smart city implementation. Moreover, this chapter examines the strategic, cultural, socioeconomic, and geographical differences that influence smart city projects worldwide. It analyzes the geographical influence and internal drivers of smart city projects, focusing on Europe, North America, Latin America, and East and South Asia. This analysis offers insights into diverse approaches to smart city development, encompassing top-down and bottom-up approaches. By examining various perspectives and experiences from smart city initiatives worldwide, this chapter sheds light on the challenges and opportunities associated with implementing smart city strategies in different contexts.

This chapter examines the impact of smart city projects on the economic and business environment, with a specific focus on their potential to foster innovation and entrepreneurship. It highlights how smart cities create an enabling environment for new businesses, start-ups, and promotes gender and green entrepreneurship, while enhancing urban properties and contributing to the overall entrepreneurial ecosystem. The exponential growth of smart city projects worldwide has underscored the critical relationship between smart cities and the entrepreneurial ecosystem for city prosperity and competitiveness. This chapter explores the interplay between smart cities and the economic environment, emphasizing their role as hubs for innovation and business creation. It analyzes the influence of smart city ecosystems and economic factors on local growth and economic outcomes, drawing on international research and factual data. This chapter evaluates the economic outcomes of smart cities, including their impact on the economic environment, business creation, start-ups, innovative and green companies, as well as gender entrepreneurship. This chapter emphasizes the significance of smart city implementation in driving economic advancements within both the urban and economic landscapes. Finally, this chapter concludes by presenting a conceptual framework that synthesizes the insights gained, providing a comprehensive understanding of the economic impact of smart cities and offering a roadmap for further exploration of this topic.