Role Of Social Network Structure Research Articles

The effect of social network structure on group anchoring bias

AbstractDecisions—whether made by individuals or groups—often involve estimating quantities, a process that is subject to anchoring bias (Tversky and Kahneman in Science 185: 1124–1131, 1974). Differences in susceptibility to anchoring bias between individuals and groups have been recently explored with the result that groups appear less biased than individuals (Meub and Proeger in Theor Decis 85:117–150, 2018). However, existing studies treat groups monolithically without taking into account their network structure—the pattern of relationships among members. The present paper investigates the effects of group social network structure on anchoring bias. Using a structured survey instrument, we gathered data on competence-based trust relationships among 264 students enrolled in a university degree program. An anchoring experiment was conducted in which some of the students made estimates as individuals, while others did so in groups of different structures. The findings provide initial evidence of differences in bias levels across variously structured groups as well as relative to individuals. Groups with highly centralized trust networks (where a single person owned everyone’s trust) showed more anchoring bias than dense groups (where everyone trusted everyone else) and sparse groups (where no one trusted any other member of the group) showed more bias than dense groups. In addition, despite previous research demonstrating groups are less susceptible than individuals to anchoring bias, this study shows a higher presence of bias in both our centralized groups and sparse groups when compared to individuals, suggesting that group structure might moderate the mitigating effect of groups on anchoring bias. The research has implications for organizational behavior and social network literature. Specifically, this study contributes to the debate on anchoring bias for group decisions by highlighting the significant role of social network structure. At the same time, it contributes to the literature on network structure and performance by providing initial evidence of how network structure affects anchoring bias susceptibility. Moreover, our study contributes to management practice by alerting managers to the dangers of centralized networks, suggesting that competence-based trust plays a vital role in the resistance to anchoring bias.

The role of social network structure and function in moderate and severe social and emotional loneliness: The Dutch SaNAE study in older adults

BackgroundLoneliness is a serious public health problem. This became even more visible during the COVID-19 pandemic. Yet, the key social network aspects contributing to loneliness remain unknown. Here, we evaluated social network structure and function and associations with (moderate/severe) social and emotional loneliness in older adults. MethodsThis cross-sectional study includes online questionnaire data (SaNAE cohort, August–November 2020), in independently living Dutch adults aged 40 years and older. For the separate outcomes of social and emotional loneliness, associations with structural social network aspects (e.g., network diversity - having various types of relationships, and density - network members who know each other), and functional social network aspects (informational, emotional, and practical social support) were assessed and risk estimates were adjusted for age, educational level, level or urbanization, comorbidities, and network size. Multivariable logistic regression analyses were stratified by sex. ResultsOf 3396 participants (55 % men; mean age 65 years), 18 % were socially lonely which was associated with a less diverse and less dense network, living alone, feeling less connected to friends, not having a club membership, and fewer emotional supporters (men only) or informational supporters (women only). 28 % were emotionally lonely, which was associated with being socially lonely, and more exclusively online (versus in-person) contacts (men only), and fewer emotional supporters (women only). ConclusionNetwork structure and function beyond the mere number of contacts is key in loneliness. Public health strategies to prevent loneliness in older adults should be sex-tailored and promote network diversity and density, club membership, informational and emotional support, and in-person contact.

Modeling the spread of infectious diseases through influence maximization.

Mathematical approaches, such as compartmental models and agent-based models, have been utilized for modeling the spread of the infectious diseases in the computational epidemiology. However, the role of social network structure for transmission of diseases is not explicitly considered in these models. In this paper, the influence maximization problem, considering the diseases starting at some initial nodes with the potential to maximize the spreading in a social network, is adapted to model the spreading process. This approach includes the analysis of network structure and the modeling of connections among individuals with probabilities to be infected. Additionally, individual behaviors that change along the time and eventually influence the spreading process are also included. These considerations are formulated by integer optimization models. Simulation results, based on the randomly generated networks and a local community network under the COVID-19, are performed to validate the effectiveness of the proposed models, and their relationships to the classic compartmental models.

Exploring the Role of Social Network Structure in Disease Risk among U.S. Long-haul Truck Drivers in Urban Areas.

Objective: Using mixed methods, we explored properties of long-haul truckers' social networks potentially influencing STI/BBI acquisition and transmission. Methods: We recruited inner-city drug and sex network members (N = 88) for interviews. Blood and urine samples and vaginal swabs were collected to test for STIs/BBIs. Data were collected on participants' role in the network (trucker, sex worker, or intermediary), sexual and substance-use behaviors, and dyadic relationships with drug and/or sex contacts. We analyzed network data using UCINET. Results: Data revealed 2 major network clusters (58 male truckers, 6 male intermediaries, and 24 female sex workers; 27.3% STI/BBI positive). Overall, 18.8% of network members had more than one type of risky relationship with the same person (multiplexity), 11.4% of dyads were between 2 STI/ BBI positive people (assortative mixing), 36.4% were between one STI/BBI positive person and one negative person (disassortative mixing), 44.3% of people were connected to more than one person who was STI/BBI positive (concurrency), and 62.5% of nodes were just one path removed from an STI/BBI positive individual (bridging). Conclusion: Despite only 27.3% of the network being STI/BBI positive, our results revealed network characteristics (and potential intervention points) that amplify risk of disease spread within trucker-centered networks.

Centrality-Based Spillover Effects

We study the role of social network structure in peer-to-peer educational spillovers by leveraging a two-year field experiment in primary schools in rural Bangladesh. We implement a randomized educational intervention--the provision of free after-school tutoring--offered to a random subsample of students in treatment schools. We exploit the experimentally induced across-classroom variation in the centrality of treated students to provide the first causal evidence of centrality-based spillover effects. We find that a one standard deviation (SD) increase in the average centrality of treated students within a classroom leads to improvements in the test scores of their untreated classmates of 0.57 SD in English and 0.62 SD in math. Further evidence indicates that more central students have higher academic ability, better social skills, and interact more with classmates on matters related to learning, which suggests that they can be more influential for their peers. In addition, we offer the private tutoring intervention to the most central students on a separate group of schools. We find that this targeted approach provides larger educational benefits both for treated and untreated students than the policy that treats a random subset of students. We conclude that targeting the most central students in a network to offer an intervention can be a cost-effective way to improve the educational outcomes of all students in a classroom.

Converging evidence

Abstract New languages emerge through interactions among people, yet the role of social network structure in language emergence is not clear, despite research from experimental semiotics, observational fieldwork, and computational modeling. To better understand the effects of social network structure on the formation of conventional referring expressions, we use a silent gesture paradigm that combines the methodological control of experimental semiotics and computational simulations with the naturalistic affordances of the human body, physical environment, and interpersonal communication. We elicited gestural referring expressions from hearing participants randomly assigned to either a richly- or sparsely-connected communicative network. Results demonstrate greater conventionalization among participants in the richly-connected condition, although this effect disappears after accounting for between-condition differences in overall number of communicative interactions. These results provide the first experimental demonstration that communicative network structure causally impacts the conventionalization of referring expressions in human participants, using a communicative modality in which human language naturally arises.

The Role of Social Network Structure in the Emergence of Linguistic Structure.

Social network structure has been argued to shape the structure of languages, as well as affect the spread of innovations and the formation of conventions in the community. Specifically, theoretical and computational models of language change predict that sparsely connected communities develop more systematic languages, while tightly knit communities can maintain high levels of linguistic complexity and variability. However, the role of social network structure in the cultural evolution of languages has never been tested experimentally. Here, we present results from a behavioral group communication study, in which we examined the formation of new languages created in the lab by micro-societies that varied in their network structure. We contrasted three types of social networks: fully connected, small-world, and scale-free. We examined the artificial languages created by these different networks with respect to their linguistic structure, communicative success, stability, and convergence. Results did not reveal any effect of network structure for any measure, with all languages becoming similarly more systematic, more accurate, more stable, and more shared over time. At the same time, small-world networks showed the greatest variation in their convergence, stabilization, and emerging structure patterns, indicating that network structure can influence the community's susceptibility to random linguistic changes (i.e., drift).

A novel approach for the design of context-aware services for social inclusion and education

BACKGROUND: The new generation networks (5G and beyond) will allow us to collect and process real-time information about a user and his context. Analyzing the adolescents’ behaviour and taking into account relations between their psychological frailty and socio-cultural context, it is possible to highlight situations of vulnerability. OBJECTIVE: It is crucial to shed light on how the nature of social relationships and the similarity among individuals play a role in the collective dynamics. METHODS: To understand these dynamics, Evolutionary Game Theory and the analysis of social networks, modeled as multiplex networks, are useful. RESULTS: Thanks to a simulative approach we evaluate the emergence and maintenance of cooperation within a class, assessing the role of social network structure and of the homophily on the dynamics. CONCLUSION: Exploiting these tools it is possible to design innovative ICT context-aware services based on collective cooperation and aimed at improving social inclusion, education and support for frail people.

How Team Emotional Intelligence Connects to Task Performance: A Network Approach

Drawing on a social network analysis, we examined the role of social network structure (i.e., friendship network density) as a mediating factor linking team average emotional intelligence (EI) with team task performance and how this relationship is influenced by intrateam trust. The results from 97 teams (466 team members) working on student projects in a business university over a semester indicated that teams with higher average EI exhibit a higher density of friendship networks and better team task performance in contexts characterized by higher intrateam trust. The study increases our understanding of the mechanism through which EI at the team level may affect team performance and provides new insights into how team managers and members might enhance the effectiveness of project teams.

The Role of Social Network Structure in Street-Level Innovation

In considering how peer relationships can aid street-level bureaucrats in doing their jobs, existing literature has emphasized the importance of peers in providing the social and emotional support required to deal with uncertain and stressful working situations. By applying a social network perspective to examine the innovative behavior of a sample of teachers in a large urban high school, this article highlights the importance of an additional factor: the location of a frontline worker’s position in the larger structure of social connections within the organization. In particular, multilevel statistical models reveal a positive association between the extent to which an experienced teacher is located in a network position that bridges across different organizational subgroups and his or her level of innovation, suggesting that experienced frontline workers may benefit from the information diversity that comes from having multiple and diverse social contacts. More generally, the study highlights the value of complementing individual and organizational insights with network-level perspectives for understanding the discretionary behavior of frontline professionals.

Justice in Self-Managing Teams: The Role of Social Networks in the Emergence of Procedural Justice Climates

Drawing upon organizational justice and social network theories, we examined the role of social network structure and content in the development of justice climates in self-managing teams, a relatively understudied context in the justice literature. Data from 79 project teams were used to test the hypotheses. Our results indicate that team instrumental network density is positively related to procedural justice climate strength and that this relationship is strengthened by low team functional background diversity. In addition, team expressive network density and team self-monitoring interact to reduce variability in member perceptions of procedural justice and thus strengthen justice climates.

Social Movements and Ecosystem Services—the Role of Social Network Structure in Protecting and Managing Urban Green Areas in Stockholm

Ernstson, H., S. Sörlin, and T. Elmqvist. 2008. Social movements and ecosystem services–the role of social network structure in protecting and managing urban green areas in Stockholm. Ecology and Society 13(2): 39. https://doi.org/10.5751/ES-02589-130239

Competitive advantages of managing an effective social network structure to stimulate innovation from a knowledge management perspective

This study attempts to elucidate the role of social network structure in moderating the relationship between knowledge management practices and innovation stimulators to derive competitive advantages during the innovation process. First, a literature review is made of theories regarding social network structure, along with a summary of the merits and limitations of each social network structure involved in knowledge transfer. Secondly, each social network structure facilitates different knowledge management practices. Finally, each knowledge management practice facilitates different innovation stimulators during each innovation period to advance corresponding competitiveness. The authors propose a framework to consolidate logic relationships above and present the framework by discussing two explanatory cases, Sun Microsystems and Amazon.com. These two enterprises can achieve competitive advantages by contingently managing an effective social network structure.