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This chapter examines the impoverishment process in three South Caucasian states: Armenia, Azerbaijan and Georgia. It uses the concept of ‘order of curtailment’ of consumption expenditures to detect the order of curtailment of expenditures in the Caucasus region. It then suggests computing poverty rates on the basis of a threshold corresponding to the curtailment of a certain number of consumption expenditures categories and compares the poverty rates obtained with those derived from more traditional approaches to the unidimensional measurement of poverty. The empirical illustrations are based on the Caucasus Barometer surveys of 2009 and 2013.

Relational thinking and spatial analyses have become highly relevant for higher education research. However, choices of research methods and specifically of statistical procedures do not often correspond to the epistemological underpinnings implied by relational perspectives. Against this background, this chapter illustrates the uses and challenges of geometric data analysis (GDA) for studying the complexities and dynamics of current spaces of higher education. GDA can be described as a set of statistical techniques that allow the identification, assessment and visualisation of complex relations in social science data. Using an investigation into the social topologies of first-year students as an example, we discuss the mathematical foundations, the step-by-step procedures of data analysis, the interpretation of results and strategies for integrating GDA into multimethod research designs. In sum, we argue that GDA does not only entail a comprehensive set of statistical instruments that permit visual analysis of relational structures, but also enables the systematic integration of qualitative and quantitative methods, hence supporting the development of innovative and coherent research designs and analytical strategies.

The authors apply topic sentiment analysis (several relatively new text analysis methods) to the study of public opinion as expressed in social media by comparing reactions to the Trayvon Martin controversy in spring 2012 by commenters on the partisan news websites the Huffington Post and Daily Caller.

Topic sentiment analysis is a text analysis method that estimates the polarity of sentiments across units of text within large text corpora (Lin & He, 2009; Mei, Ling, Wondra, Su, & Zhai, 2007).

We apply topic sentiment analysis to public opinion as expressed in social media by comparing reactions to the Trayvon Martin controversy in spring 2012 by commenters on the partisan news websites the Huffington Post and Daily Caller. Based on studies that depict contemporary news media as an “outrage industry” that incentivizes media personalities to be controversial and polarizing (Berry & Sobieraj, 2014), we predict that high-profile commentators will be more polarizing than other news personalities and topics.

Results of the topic sentiment analysis support this prediction and in so doing provide partial validation of the application of topic sentiment analysis to online opinion.

Independent actors operating through peer-to-peer sharing economy platforms co-create service experiences, such as shared car-rides or home-stays. Emotional labor among both parties, manifested in the mutual enactment of socially desirable behavior, is essential in ensuring that these experiences are successful. However, little is known about emotional labor practices and about how sharing economy platforms enforce emotional labor practices among independent actors, such as guests, hosts, drivers, or passengers. To address this research gap, we follow a mixed methods approach. We combine survey research among Airbnb and Uber users with content analysis of seven leading sharing economy platforms. The findings show that (1) users perform emotional labor despite not seeing is as necessarily desirable and (2) platforms actively encourage the performance of emotional labor practices even in the absence of direct formal control. Emotional labor practices are encouraged through (hard) design features such as mutual ratings, reward systems, and gamification, as well as through more subtle (soft) normative framing of desirable practices via platform and app guidelines, tips, community sites, or blogs. Taken together, these findings expand our understanding of the limitations of peer-to-peer sharing platforms, where control over the service experience and quality can only be enforced indirectly.

At least 35 years have passed since Slovic's (1987) seminal article on the ‘Perception of Risk’, wherein the conceptual foundations for understanding general risk and the psychometric properties underlying how individuals perceive risks were laid. Over the same time span, research on risk perception in the context of travel has become voluminous and recurrent. It is therefore fitting that in a modern, post-COVID age, Slovic's theory of risk perception is re-examined in the travel context, given the recent dramatic transformation of travel, the emergence of novel tourism-related risks and persistent scholarly attempts to understand travel risk theory. Using modern data mining methods and content analysis techniques, this chapter examines the stability and validity of long-standing categories and taxonomies of perceived travel risks, based on data archived in a sizeable database of scholarly studies related to travel risk (n = 17,790 studies), across an extensive 35-year period from 1990 to 2022. Findings infer two higher-order dimensions that likely underpin the taxonomic organization and relational ordering of different travel risk types and clusters. Findings also suggest a possible shift from Slovic's original theory in the way risks are perceived, at least in the travel context.

We reformulate regression modeling so that ideas often associated with field theory and social network analysis can be brought to bear at every stage in the computation and interpretation of regression coefficients in studies of organizations. Rather than “transcending” general linear reality, we seek to get more out of it. We formulate a dual to regression modeling based on using the variables to learn about the cases. We illustrate our ideas by applying the new approach to a database of hundreds of violent extremist organizations, focusing on understanding which groups use or pursue unconventional weapons (chemical, biological, radiological, nuclear).

Purpose: The study’s objective is to look at the link between money laundering and economic and financial performance, emphasising the effectiveness of the literature and possible later research directions using science mapping, which allows for scientific knowledge analysis.

Need for the Study: This study is related to a better understanding of the field’s historical evolution in terms of publications.

Methodology: This study used bibliometric approaches to analyse a sample of 660 studies from the Web of Science between 1994 and 2022, concentrating on keywords, author, paper, journal, and subject analysis. This study focused on performance analysis and scientific mapping of articles using the R package.

Findings: The empirical results indicated that the research field’s primary issues include corporate governance, fraud, machine learning, fraud detection, financial fraud, financial statement, corruption, earnings management, ethics, governance, financial reporting, bankruptcy, internal control, or performance. M. S. Beasly, D. B. Farber, E. M. Fich, R. Romano, and A. Shivdasani are the most well-known authors on the issue of money laundering and financial and economic performance. At the same time, the most typical journals are the Journal of Business Ethics, Journal of Money Laundering Control, Accounting Review, Journal of Financial Economics, and Journal of Corporate Finance.

Practical Implications: This study will act as a guide for researchers of various fields to evaluate the development of scientific publications in a particular theme over time, especially for those who are in the field of money laundering and financial performance.

The difficulties that MR poses for comparativists were anticipated 40 years ago in Sidney Verba's essay “Some Dilemmas of Comparative Research”, in which he called for a “disciplined configurative approach…based on general rules, but on complicated combinations of them” (Verba, 1967, p. 115). Charles Ragin's (1987) book The Comparative Method eloquently spelled out the mismatch between MR and causal explanation in comparative research. At the most basic level, like most other methods of multivariate statistical analysis MR works by rendering the cases invisible, treating them simply as the source of a set of empirical observations on dependent and independent variables. However, even when scholars embrace the analytical purpose of generalizing about relationships between variables, as opposed to dwelling on specific differences between entities with proper names, the cases of interest in comparative political economy are limited in number and occupy a bounded universe.² They are thus both knowable and manageable. Consequently, retaining named cases in the analysis is an efficient way of conveying information and letting readers evaluate it.³ Moreover, in practice most producers and consumers of comparative political economy are intrinsically interested in specific cases. Why not cater to this interest by keeping our cases visible?

This chapter presents the impact of the Covid-19 pandemic on the course of studies and life career skills development of undergraduate students at the University of Macedonia, a mid-sized public Greek University. It describes a multivariate methodology research that investigated how the students, first-hand experienced the unexpected changes from face-to-face on campus to synchronous online education during the lockdown and how they coped with these changes. Change is considered and described as a main component within current life and career trajectories addressing chaotic and unpredictable circumstances while Chaos Theory of Careers (CTC) offers the theoretical background of the chapter. The research followed the mixed methods paradigm: a multilevel embedded sequential explanatory design including a participant selection model and multivariate data analysis methods. A survey (N = 621) was conducted; individual interviews and focus groups’ discussions further explained the quantitative findings. The emerging clusters of students revealed similarities in feelings, motivation, adaptation, and life career skills development. The first cluster comprised of older, digitally high-skilled students, with the required technological equipment, adaptable to change, self-regulated, strongly in favor of synchronous online education; in the second cluster were grouped the younger, digitally medium-skilled students, who regularly participated in both modalities, critically recognized the advantages of either one, feeling strongly in favor of a combination; finally, the third cluster included digitally medium-skilled students who found serious difficulty in using online platforms, dissatisfied with social isolation and distant interaction, strongly preferring face-to-face instruction, valuing direct physical contact, social connection, and networking.