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Statistical models for social networks, such as exponential random graphs (ERGMs), have increasingly been used by organizational scholars to study the social interactions inside organizations, departments, and teams. While such models have been successful in providing insights about the local processes that underlie these interactions (such as homophily, reciprocity or transitivity), an additional interesting avenue for research focuses on the role of group-level contextual variables (such as the climate or composition of a team or organization) when considering a multitude of teams or organizations. In this paper we show how, in a team context, integrating team-level factors in a multilevel framework (i.e., a Bayesian hierarchical ERGM) enables us to answer questions about: 1) how team-level contextual factors might provide alternative explanations for the emergence of intra-team ties, and 2) whether any variation in local tendencies between teams might be dependent on such team-level variables (i.e., cross-level interactions). Using data collected among 103 MBA students who were grouped into 18 teams of 5–7 members to work on a project, we study the impact of members’ expertise and perceived psychological safety on advice seeking behavior. At a local level, we focus on the effect of these nodal attributes for homophily and differences in advice seeking and giving, while at the team level we incorporate the average expertise and the team’s psychological safety climate. Our results show that expert members are more likely to be the recipient of advice ties, but also that this effect is more pronounced in teams where the overall level of expertise is high. For psychological safety, we find that a high psychological safety climate impacts the advice relations among all members in the group, not solely those who themselves perceive a high level of psychological safety, suggesting that the team climate has an impact on all its members. This illustrates how a Bayesian hi

This paper presents the data collection method and introduces the dataset about consumers’ consider-then-choose behaviors in the household vacuum cleaner market. First, we designed a questionnaire that collected participants’ consideration and choice data, social network data, demographic information, and preferences for product features. In addition, we obtained data on vacuum cleaner product features through web scraping from online shopping websites. After data cleaning and processing, the resulting dataset enables investigation into customer preferences in two stages, namely the consideration and choice stages and the impact of social influence on the two-stage decision-making process. This dataset is unique as it is the first of its kind to collect both customers’ revealed preferences in a two-stage decision-making process and their ego social networks. This enables the modeling of customer preferences while accounting for social influence. The published survey questionnaire can be used as a template to collect data on other products in support of customer preferences modeling and the design for market systems.

Stochastic actor-oriented models (SAOMs) are a modelling framework for analysing network dynamics using network panel data. This paper extends the SAOM to the analysis of multilevel network panels through a random coefficient model, estimated with a Bayesian approach. The proposed model allows testing theories about network dynamics, social influence, and interdependence of multiple networks. It is illustrated by a study of the dynamic interdependence of friendship networks and minor delinquency. Data were available for 126 classrooms in the first year of secondary school, of which 82 were used, containing relatively few missing data points and having not too much network turnover.

We consider data with multiple observations or reports on a network in the case when these networks themselves are connected through some form of network ties. We could take the example of a cognitive social structure where there is another type of tie connecting the actors that provide the reports; or the study of interpersonal spillover effects from one cultural domain to another facilitated by the social ties. Another example is when the individual semantic structures are represented as semantic networks of a group of actors and connected through these actors’ social ties to constitute knowledge of a social group. How to jointly represent the two types of networks is not trivial as the layers and not the nodes of the layers of the reported networks are coupled through a network on the reports. We propose to transform the different multiple networks using line graphs, where actors are affiliated with ties represented as nodes, and represent the totality of the different types of ties as a multilevel network. This affords studying the associations between the social network and the reports as well as the alignment of the reports to a criterion graph. We illustrate how the procedure can be applied to studying the social construction of knowledge in local flood management groups. Here we use multilevel exponential random graph models but the representation also lends itself to stochastic actor-oriented models, multilevel blockmodels, and any model capable of handling multilevel networks.

The network influence model is a model for binary outcome variables that accounts for dependencies between outcomes for units that are relationally tied. The basic influence model was previously extended to afford a suite of new dependence assumptions and because of its relation to traditional Markov random field models it is often referred to as the auto logistic actor-attribute model (ALAAM). We extend on current approaches for fitting ALAAMs by presenting a comprehensive Bayesian inference scheme that supports testing of dependencies across subsets of data and the presence of missing data. We illustrate different aspects of the procedures through three empirical examples: masculinity attitudes in an all-male Australian school class, educational progression in Swedish schools, and unemployment among adults in a community sample in Australia., Funding Agencies|Department of DefenseUnited States Department of Defense [ARO W911NF-21-1-0335]; Melbourne climate futures accelerator grant; [NSF-CMMI-2005661]; [APP1073041]

There is a tendency to consider covert networks as separate from overt networks. Drawing on data from the Democratic Republic of the Congo, we demonstrate that this is not the case and identify how covert and overt networks are mutually constitutive. While most studies of African brokers have relied on network metaphors like Big Men and social membranes, we consider the embeddedness of covert networks in overt networks explicitly. We perform two analyses on a large original dataset encompassing 396 partially overlapping ego-nets obtained from a hybrid link-tracing design. An ego-net analysis reveals a large degree of homophily and a deep embeddedness of the different networks. A multilevel exponential random graph model fitted to the reconstructed network of a 110-node subset shows that demobilised combatants are the actors likely to broker between armed groups, state forces, and civilian blocs, suggesting their capacity to broker peace or foment war., Funding Agencies|UK Aid from the UK Department for International Development (DFID); Centre for Public Authority and International Development at LSE; ESRC GCRFUK Research & Innovation (UKRI)Economic & Social Research Council (ESRC) [ES/P008038/1]; Collecting and analyzing secondary covert social network data project - Leverhulme TrustLeverhulme Trust [RPG-2013-140]; DFID-ESRC Poverty Alleviation ProgrammeUK Research & Innovation (UKRI)Economic & Social Research Council (ESRC)

Collaboration between members of different criminal groups is an important feature of crime that is considered organised, as it allows criminals to access resources and skills in order to exploit illicit economic opportunities. Collaboration across criminal groups is also difficult and risky due to the lack of institutions supporting peaceful cooperation in illicit markets. Thus cross-group collaboration has been thought to take place mostly among small and transient groups. This paper determines whether and under what conditions members of different, larger organised crime groups collaborate with one another. To do so we use intelligence data from the Canadian province of Alberta, centering on criminals and criminal groups engaged in multiple crime types in multiple geographic locations. We apply a multilevel network analytical framework and exponential random graph models using Bayesian techniques to uncover the determinants of cross-group criminal collaboration. We find cross-group collaboration depends not only on co-location, but also on the types of groups to which the criminals are affiliated, and on illicit market overlap between groups. When groups are operating in the same geographically-situated illicit markets their members tend not to collaborate with one another, providing evidence for the difficulty or undesirability of cross-group collaboration in illicit markets. Conversely, members of Outlaw Motorcycle Gangs are more likely to collaborate across groups when markets overlap, suggesting the superior capacity and motivation of biker gangs to coordinate criminal activity. Our paper contributes to the understanding of criminal networks as complex, emergent, and spatially embedded market phenomena., Funding Agencies|Leverhulme TrustLeverhulme Trust [RPG-2013-140]

The tools of social network analysis offer a promising framework for studying fictional texts and the relational activity of the characters therein. The goal of this paper is to offer both a conceptual refinement of the project of measuring the centrality of characters within narratives using network tools, as well as the proposal of a novel measure with which to do so. Conceptually, we argue that as questions of time, order and sequence are central in narratives, measures of characters narrative importance should be based on dynamic network representations which respect the time-ordering of narrative events. We suggest a directed dynamic measure of relative character importance based on character interactions and illustrate it through an examination of gender in the 2015 film Star Wars: The Force Awakens. We find that the measure helps illuminate important narrative dynamics which cannot be captured by static measures, and presents a platform on which future character network research can productively build.