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To analyze networks of social interactions between the residents of a therapeutic community (TC) for women and the way, in which such interactions predict the discussion of issues that arise in treatment.

In total, 50 residents of a corrections-based TC for women were surveyed on the peers with whom they socialized informally, shared meals, shared letters from home and discussed issues that arose in treatment over a 12 h period. The data were analyzed using exponential random graph models (ERGM).

Reciprocity occurred in all networks while transitivity (a tendency of two residents who are connected to both connect to a third peer) occurred in all networks measuring informal social interactions. When controlling for reciprocity and transitivity, residents avoided spending social time or sharing meals with the same peers. There was no evidence of homophily by race, age or years of education. Homophily by entrance time and case manager occurred in social time. Case manager homophily occurred in the discussion of treatment issues but disappeared when controlling for social time and sharing letters from home.

Social networks in this TC arise from factors endogenous to the TC itself. It should be possible to determine the characteristics of optimal social networks in TCs. External validity is limited.

It should be possible to intervene to optimize the social networks of TC residents.

This is the first ERGM analysis of both informal and formal interactions in a TC.

We study organizational vocabularies as complex social structures emerging from the association between organizational participants and words they use to describe and make sense of their experiences at work. Using data that we have collected on the association between managers in a multi-unit international company and words they use to describe their organizational units and the overall company, we examine the relational micro-mechanisms underlying the observed network structure of organizational vocabularies. We find that members of the same subsidiary tend to become more similar in terms of the words they use to describe their units. Members of the same subsidiary, however, do not use the same words to describe the corporate group. Consequently, the structure of organizational vocabularies tends to support consistent local interpretations, but reveals the presence of divergent meanings that organizational participants associate with the superordinate corporate group.

Informal learning networks are critical to response to calls for practitioners to reskill and upskill in off-site construction projects. With the transition to the coronavirus disease 2019 (COVID-19) pandemic, social media-enabled online knowledge communities play an increasingly important role in acquiring and disseminating off-site construction knowledge. Proximity has been identified as a key factor in facilitating interactive learning, yet which type of proximity is effective in promoting online and offline knowledge exchange remains unclear. This study takes a relational view to explore the proximity-related antecedents of online and offline learning networks in off-site construction projects, while also examining the subtle differences in the networks' structural patterns.

Five types of proximity (physical, organizational, social, cognitive and personal) between projects members are conceptualized in the theoretical model. Drawing on social foci theory and homophily theory, the research hypotheses are proposed. To test these hypotheses, empirical case studies were conducted on two off-site construction projects during the COVID-19 pandemic. Valid relational data provided by 99 and 145 project members were collected using semi-structured interviews and sociometric questionnaires. Subsequently, multivariate exponential random graph models were developed.

The results show a discrepancy arise in the structural patterns between online and offline learning networks. Offline learning is found to be more strongly influenced by proximity factors than online learning. Specifically, physical, organizational and social proximity are found to be significant predictors of offline knowledge exchange. Cognitive proximity has a negative relationship with offline knowledge exchange but is positively related to online knowledge exchange. Regarding personal proximity, the study found that the homophily effect of hierarchical status merely emerges in offline learning networks. Online knowledge communities amplify the receiver effect of tenure. Furthermore, there appears to be a complementary relationship between online and offline learning networks.

Proximity offers a novel relational perspective for understanding the formation of knowledge exchange connections. This study enriches the literature on informal learning within project teams by revealing how different types of proximity shape learning networks across different channels in off-site construction projects.

Existing studies on crowdsourcing have focused on analyzing isolated contributions by individual participants and thus collaboration dynamics among them are under-investigated. The value of implementing crowdsourcing in problem solving lies in the aggregation of wisdom from a crowd. This study examines how marginality affects collaboration in crowdsourcing.

With population level data collected from a global crowdsourcing community (openideo.com), this study applied social network analysis and in particular bipartite exponential random graph modeling (ERGM) to examine how individual level marginality variables (measured as the degree of being located at the margin) affect the team formation in collaboration crowdsourcing.

Significant effects of marginality are attributed to collaboration skills, number of projects won, community tenure and geolocation. Marginality effects remain significant after controlling for individual level and team level attributes. However, marginality alone cannot explain collaboration dynamics. Participants with leadership experience or more winning ideas are also more likely to be selected as team members.

The core contribution this research makes is the conceptualization and definition of marginality as a mechanism in influencing collaborative crowdsourcing. This study conceptualizes marginality as a multidimensional concept and empirically examines its effect on team collaboration, connecting the literature on crowdsourcing to online collaboration.

This paper presents a critical review of the economic network analysis methods and their applications to maritime transport. A network can be presented in terms of its structure, topology, characteristics as well as the connectivity with different measures such as density, degree distribution, centrality (degree, betweenness, closeness, eigenvector and strength), clustering coefficient, average shortest path length and assortative. Various models such as the random graph model, block model, and ERGM can be used to analyse and explore the formation of a network and interaction between nodes. The review of the existing theories and models has found that, while these models are rather computationally intensive, they are based on some rather restrictive assumption on network formation and relationship between ports in the network at the local and global levels that require further investigation. Based on the review, a conceptual framework for maritime transport network research is developed, and the applications for future research are also discussed.

In Latin America and the Caribbean, the access of students to higher education has presented an extraordinary growth over the past fifteen years. This rapid growth has presented a challenge for increasing the system resources and capabilities while maintaining its quality. As a result, the networked universities (NUs) organized themselves as a collaborative network, and they have become an interesting model for facing the complexity driven by globalization, rapidly changing technology, dynamic growth of knowledge and highly specialized areas of expertise. In this article, we studied the NU named Red Universitaria Mutis (Red Mutis) with the aim of characterizing the collaboration and integration structure of the network.

Network analytic methods (visual analysis, positional analysis and a stochastic network method) were used to characterize the organizational structure and robustness of the network, and to identify what variables or structural tendencies are related to the likelihood that specific areas of a university would collaborate.

Red Mutis is a good example of regional NUs that could take advantage of the strengths, partnerships, information and knowledge of the regional and international universities that form the network. Analyses showed that Red Mutis has a differentiated structure consisting of academic and non-academic university areas with a vertical coordination (by steering and management) of the different university areas.

The methodology could be used as a framework to analyze and strengthen other strategic alliances between universities and as a model for the development of other NU in local and global contexts.

The health equity and prosperity of communities is closely linked to the effectiveness and success of local health coalitions. Social network analysis (SNA) is one mechanism to quantify and understand the factors leading to collaboration and effectiveness within these coalitions. This study aims to investigate network characteristics associated with perceived success and satisfaction in a health coalition and determine significant factors related to organizational collaborations.

This study examined the Olympic Peninsula Healthy Community Coalition (OPHCC) which aims to prevent chronic disease in rural Clallam County, Washington. Representatives (n = 21) from member organizations (n = 18) were asked to report on organization characteristics, perceived satisfaction in coalition activities, perceived success toward coalition's mission, and collaborations with other organizations in the coalition. Multilevel modeling used to analyze whether an organization's position within the coalition network was associated with their perceived satisfaction and perceived success. Exponential random graph modeling was used to examine what factors may impact collaboration ties between coalition members.

Organization representatives reported a total of 252 collaboration ties. In multilevel models, organization characteristics and network centrality scores accounted for between 61 and 68% of variance displayed in satisfaction scores and 45–61% of variance in perceived success scores. Exponential random graph modeling revealed activity level, for-profit status, and transitivity as significant factors in collaborative tie presence.

Encouraging consistent active participation, a balance of organizational type, and projects which require more than two collaborators may provide an environment for collaborative ties between organizations.

This paper presents a critical review of the economic network analysis methods and their applications to maritime transport. A network can be presented in terms of its structure, topology, characteristics as well as the connectivity with different measures such as density, degree distribution, centrality (degree, betweenness, closeness, eigenvector and strength), clustering coefficient, average shortest path length and assortative. Various models such as the random graph model, block model, and ERGM can be used to analyse and explore the formation of a network and interaction between nodes. The review of the existing theories and models has found that, while these models are rather computationally intensive, they are based on some rather restrictive assumption on network formation and relationship between ports in the network at the local and global levels that require further investigation. Based on the review, a conceptual framework for maritime transport network research is developed, and the applications for future research are also discussed.

The purpose of this study is to show that social relations in a corporate governance platform between members of supervisory boards and between members of supervisory and executive board tiers can serve as an alternative viewpoint for understanding mechanisms of social selection in corporate governance networks. The study shows that through the lenses of social network analysis, it is possible to identify and understand how the process of corporate governance member selection unfolds within companies and how that selection process may have been potentially influenced by the cross-board relations, such as interlocking directorships.

To estimate network parameters and attribute effects of network tie emergence, this study has used exponential random graph models (ERGMs) on corporate governance data of Danish publicly listed companies. Econometric models are applied to estimate parameter statistics which serve further to explain tendencies of tie emergence.

The results of this study reveal that the process of selection of both supervisory boards and executive directors is interdependent. Also, the study showed that board members are more likely to select popular supervisory board members and top managers who have their expertise gained through multiple companies affiliated with multiple industries. However, these conditions for CEO selection apply only to the extent to which they have their experience gained from multiple companies but not multiple industries.

On one hand, this study demonstrates that being a dynamic practitioner who is exposed to diverse corporate environments by being affiliated with different companies belonging to different industries generally increases practitioners visibility in the corporate governance network, and therefore their attractiveness to boards of directors. On the other hand, the results show that the research on board assemblage, nowadays, should be rather observed through the methodology of social network analysis as the method gives an opportunity to understand structures through relations, from which the executive tier should not be exempted as well.