Achim Edelmann, T. Wolff, Danielle Montagne
et al.
The integration of social science with computer science and engineering fields has produced a new area of study: computational social science. This field applies computational methods to novel sources of digital data such as social media, administrative records, and historical archives to develop theories of human behavior. We review the evolution of this field within sociology via bibliometric analysis and in-depth analysis of the following subfields where this new work is appearing most rapidly: (a) social network analysis and group formation; (b) collective behavior and political sociology; (c) the sociology of knowledge; (d) cultural sociology, social psychology, and emotions; (e) the production of culture; (f) economic sociology and organizations; and (g) demography and population studies. Our review reveals that sociologists are not only at the center of cutting-edge research that addresses longstanding questions about human behavior but also developing new lines of inquiry about digital spaces as well. We conclude by discussing challenging new obstacles in the field, calling for increased attention to sociological theory, and identifying new areas where computational social science might be further integrated into mainstream sociology.
Economic policy uncertainty has been increasing globally, with consequences for financial sector stability. This paper investigates its influence on the risk-taking behavior of banks. The study examines the functional form of responses of banks to economic policy uncertainty and explores how regulatory quality and safety nets change bank behavior in periods of high uncertainty.
We utilize data from 1999 to 2023 of 796 banks in 21 countries, employing a quadratic two-step system GMM estimation technique to evaluate the impact of economic policy uncertainty on banks' risk-taking. Using the U-test, we confirm the nonlinear relationship and identify its threshold point. Finally, we show the consistency of the estimates by controlling for multiple major crisis periods during the sample period.
We find that economic policy uncertainty generally increases risk-taking among banks. However, beyond a certain point, further increases in economic policy uncertainty could lead to diminishing returns and heightened risk aversion, resulting in decreased risk-taking behavior. Stronger regulatory quality mitigates this effect; however, the reduction in risk-taking is less pronounced when economic policy uncertainty increases. Safetynets moderate the relationship by impacting bank risk-taking sensitivity. Additionally, we find cross-country heterogeneity in the size of economic policy uncertainty and risk-taking. Lastly, we find that the nonlinear effects are robust after controlling for major events like the global financial crisis, the eurozone crisis, COVID-19, and the Ukraine war.
We provide evidence of nonlinearity in the nexus of economic policy uncertainty, regulatory frameworks, safety nets, and bank risk-taking behavior. The findings underscore the significance of robust regulatory quality and safety nets in moderating banks' risk-taking behavior during economic policy uncertainty.
Abstract Theoretical frameworks associated with science and technology studies (STS) are becoming increasingly prominent in social science energy research, but what do they offer? This review provides a brief history of relevant STS concepts and frameworks and a structured analysis of how STS perspectives are appearing in energy social science research and how energy-related research is appearing in social science STS. Drawing from an initial body of 262 journal articles and books with a stratified sample of 68 published from 2009 to mid-2019, the review identifies four major groups of perspectives: (1) STS-related cultural analysis, especially the study of sociotechnical imaginaries; (2) STS-related policy analysis, such as research on the social construction of risks and standards and on the performativity of economic models; (3) STS perspectives on public participation processes, expert-public relations, and mobilized publics; and (4) the study of sociotechnical systems, including large technological systems, the politics of design, and users and actor-networks. Connections among the perspectives and the value for energy social science research are also critically discussed.
As the prevalence of generative artificial intelligence (GenAI) in the service sector continues to grow, the impact of the language style and recovery strategies utilized during service failures remains insufficiently explored. This study, grounded in the theory of social presence and dual-process theory, employed a mixed-method approach combining questionnaire surveys and event-related potential (ERP) experiments to investigate the effect of different language styles (rational vs. humorous) and recovery strategies (gratitude vs. apology) on users’ willingness to forgive during the GenAI service recovery process. It further delves into the chained mediating role of perceived sincerity and social presence in this process. The findings revealed that a humorous language style was more effective in enhancing users’ willingness to forgive compared to a rational style, primarily through the enhancement of users’ perceived sincerity and sense of social presence; recovery strategies played a moderating role in this process, with the positive impact of perceived sincerity on social presence being significantly amplified when the GenAI service adopted an apology strategy. ERP results indicated that a rational language style significantly induced a larger N2 component (cognitive conflict) in apology scenarios, while a humorous style exhibited higher amplitude in the LPP component (positive emotional evaluation). This research unveils the intricate relationships between language style, recovery strategies, and users’ willingness to forgive in the GenAI service recovery process, providing important theoretical foundations and practical guidance for designing more effective GenAI service recovery strategies, and offering new insights into developing more efficacious GenAI service recovery tactics.