The Structure of Social Networks and Its Link to Higher Education Students’ Socio-Emotional Loneliness During COVID-19

Lonely students typically underperform academically. According to several studies, the COVID-19 pandemic is an important risk factor for increases in loneliness, as the contact restrictions and the switch to mainly online classes potentially burden the students. The previously familiar academic environment (campus), as well as the exchange with peers and lecturers on site, were no longer made available. In our cross-sectional study, we examine factors that could potentially counteract the development of higher education student loneliness during the COVID-19 pandemic from a social network perspective. During the semester, N = 283 students from across all institutional faculties of a German comprehensive university took part in an online survey. We surveyed their social and emotional experiences of loneliness, their self-reported digital information-sharing behavior, and their current egocentric networks. Here, we distinguished between close online contacts (i.e., mainly online exchanges) and close offline contacts (i.e., mainly in-person face-to-face exchanges). In addition, we derived the interconnectedness (i.e., the densities of the egocentric networks) and heterogeneity (operationalized with the entropy) of students’ contacts. To obtain the latter, we used a novel two-step method combining t-distributed stochastic neighbor embedding (t-SNE) and cluster analysis. We explored the associations of the aforementioned predictors (i.e., information-sharing behavior, number of online and offline contacts, as well as interconnectedness and heterogeneity of the close contacts network) on social and emotional loneliness separately using two hierarchical multiple linear regression models. Our results suggest that social loneliness is strongly related to digital information-sharing behavior and the network structure of close contacts. In particular, high information-sharing behavior, high number of close contacts (whether offline or online), a highly interconnected network, and a homogeneous structure of close contacts were associated with low social loneliness. Emotional loneliness, on the other hand, was mainly related to network homogeneity, in the sense that students with homogeneous close contacts networks experienced low emotional loneliness. Overall, our study highlights the central role of students’ close social network on feelings of loneliness in the context of COVID-19 restrictions. Limitations and implications are discussed.

Determinants of Knowledge Transfer in Egocentric Networks. Comparative Analysis of Professions

This paper investigates determinants of knowledge transfer in egocentric networks of knowledge recipient and knowledge provider, what is crucial to knowledge management in organisations. Knowledge transfer is assumed to depend on knowledge work, networking competence, and the subject’s profession: teacher, Information Technology (IT) professional, or physician. The paper reports result of a quantitative study among samples of mentioned professionalists. Regression models testing, including mediation and moderation, were performed. The findings indicate that knowledge transfer in the egocentric network of the knowledge recipient increases along with knowledge work, but only when it is mediated by networking competence. Analyses in each profession support a partial mediation in the case of IT professionals and teachers. Knowledge transfer in egocentric network of the knowledge provider increases along with knowledge work of the provider. In the case of physicians, knowledge transfer in the providers’ and recipients’ knowledge networks is affected neither by knowledge work nor by networking competence.

The Structure of Social Networks and Its Link to Higher Education Students' Socio-Emotional Loneliness During COVID-19

Lonely students typically underperform academically. According to several studies, the COVID-19 pandemic is an important risk factor for increases in loneliness, as the contact restrictions and the switch to mainly online classes potentially burden the students. The previously familiar academic environment (campus) as well as the exchange with peers and lecturers on site were no longer made available. In our study, we examine factors that could potentially counteract the development of higher education student loneliness during the COVID-19 pandemic from a social network perspective. During the semester, N = 283 students from across all institutional faculties of a German comprehensive university took part in an online survey. We surveyed their social and emotional experiences of loneliness, their self-reported digital skills, and their current egocentric networks. We distinguished between close online contacts (i.e., mainly online exchanges) and close offline contacts (i.e., mainly in situ exchanges). In addition, we derived the interconnectedness (i.e., the densities of the egocentric networks) and diversity (operationalized with the entropy) of students’ contacts. The results of correlation analyses and hierarchical linear regressions indicate that strong digital skills are related to both a higher number of online contacts and to lower social and emotional experiences of loneliness. Regardless of whether offline or online, the number of reported contacts is indicative of a lower experience of social loneliness. A well-connected network related to lower experiences of social but not emotional loneliness. Finally, findings suggest that homogenous networks tend to be related with lower experiences of both social and emotional loneliness. Overall, our study indicates that barriers to online communication might be mitigating factors to consider when assessing the impact of the COVID-19 pandemic on student loneliness.

Best Practices for Modeling Egocentric Social Network Data and Health Outcomes

Objective/Aim: We describe best practices for modeling egocentric networks and health outcomes using a five-step guide. Background: Social network analysis (SNA) is common in social science fields and has more recently been used to study health-related topics including obesity, violence, substance use, health organizational behavior, and healthcare utilization. SNA, alone or in conjunction with spatial analysis, can be used to uniquely evaluate the impact of the physical or built environment on health. The environment can shape the presence, quality, and function of social relationships with spatial and network processes interacting to affect health outcomes. While there are some common measures frequently used in modeling the impact of social networks on health outcomes, there is no standard approach to social network modeling in health research, which impacts rigor and reproducibility. Methods: We provide an overview of social network concepts and terminology focused on egocentric network data. Egocentric, or personal networks, take the perspective of an individual who identifies their own connections (alters) and also the relationships between alters. Results: We describe best practices for modeling egocentric networks and health outcomes according to the following five-step guide: (1) model selection, (2) social network exposure variable and selection considerations, (3) covariate selection related to sociodemographic and health characteristics, (4) covariate selection related to social network characteristics, and (5) analytic considerations. We also present an example of SNA. Conclusions: SNA provides a powerful repertoire of techniques to examine how relationships impact attitudes, experiences, and behaviors—and subsequently health.

Detecting problematic transactions in a consumer-to-consumer e-commerce network

Providers of online marketplaces are constantly combatting against problematic transactions, such as selling illegal items and posting fictive items, exercised by some of their users. A typical approach to detect fraud activity has been to analyze registered user profiles, user’s behavior, and texts attached to individual transactions and the user. However, this traditional approach may be limited because malicious users can easily conceal their information. Given this background, network indices have been exploited for detecting frauds in various online transaction platforms. In the present study, we analyzed networks of users of an online consumer-to-consumer marketplace in which a seller and the corresponding buyer of a transaction are connected by a directed edge. We constructed egocentric networks of each of several hundreds of fraudulent users and those of a similar number of normal users. We calculated eight local network indices based on up to connectivity between the neighbors of the focal node. Based on the present descriptive analysis of these network indices, we fed twelve features that we constructed from the eight network indices to random forest classifiers with the aim of distinguishing between normal users and fraudulent users engaged in each one of the four types of problematic transactions. We found that the classifier accurately distinguished the fraudulent users from normal users and that the classification performance did not depend on the type of problematic transaction.

The impact of Brexit on London’s entrepreneurial ecosystem: The case of the FinTech industry

This paper analyses how different entrepreneurial actors respond to political uncertainty and changing institutional settings. Moreover, it discusses the impact of those actor-level responses on the resilience of entrepreneurial ecosystems (EEs), focusing on how they affect the diversity of and the connectivity among its actors. To address these questions, the paper examines how the decision of the United Kingdom to leave the European Union has influenced the financial technology (FinTech) industry in London, applying data collected from in-depth interviews, covering different groups of stakeholders in London’s FinTech industry, such as angel investors, banks, legal advisers, lobby organizations and private companies. Our results show that political uncertainty and the prospect of institutional change can trigger actor-level responses, which have the potential to modify the diversity as well as the local and non-local connectivity of an EE. Moreover, we demonstrate that the nature of strategic responses of entrepreneurial actors varies significantly, depending on their firms’ characteristics, such as age, size, product specialization and the structure of their egocentric networks. With regard to the latter, our results show that anchor firms play an important role in other firms’ egocentric networks and have the power to shape their strategic responses.

Collecting egocentric network data with visual tools: A comparative study

When collecting egocentric network data, visual representations of networks can function as a cognitive aid for depicting relationships, helping to maintain an overview of the relationships, and keeping the attention of the interviewees. Additionally, network maps can serve as a narration generator in qualitative and in mixed-methods studies. While varying visual instruments are used for collecting egocentric network data, little is known about differences among visual tools concerning the influence on the resulting network data, the usability for interviewees, and data validity. The article provides an overview of existing visually oriented tools that are used to collect egocentric networks and discusses their functions, advantages, and limitations. Then, we present results of an experimental study where we compare four different visual tools with regard to networks elicited, manageability, and the impact of follow-up questions. In order to assess the manageability of the four tools, we used the thinking aloud method. The results provide evidence that the decision in favor of a specific visual tool (structured vs. unstructured) can affect the size and composition of the elicited networks. Follow-up questions greatly affect the elicited networks and follow-up cues can level out differences among tools. Respondents tend to prefer the concentric circles tool, with some differences in preferences and manageability of tools between participants with low and those with high socioeconomic status. Finally, assets and drawbacks of the four instruments are discussed with regard to data quality and crucial aspects of the data collection process when using visual tools.

Assessing the stability of egocentric networks over time using the digital participant-aided sociogram tool Network Canvas

This paper examines the stability of egocentric networks as reported over time using a novel touchscreen-based participant-aided sociogram. Past work has noted the instability of nominated network alters, with a large proportion leaving and reappearing between interview observations. To explain this instability of networks over time, researchers often look to structural embeddedness, namely the notion that alters are connected to other alters within egocentric networks. Recent research has also asked whether the interview situation itself may play a role in conditioning respondents to what might be the appropriate size and shape of a social network, and thereby which alters ought to be nominated or not. We report on change in these networks across three waves and assess whether this change appears to be the result of natural churn in the network or whether changes might be the result of factors in the interview itself, particularly anchoring and motivated underreporting. Our results indicate little change in average network size across waves, particularly for indirect tie nominations. Slight, significant changes were noted between waves one and two particularly among those with the largest networks. Almost no significant differences were observed between waves two and three, either in terms of network size, composition, or density. Data come from three waves of a Chicago-based panel study of young men who have sex with men.