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The distinction between network theories and theories of networks is particularly salient in studying social status because social status is both a consequence and an antecedent of network ties. Status is a consequence of network ties because it is conferred by interdependent acts of deference connecting a sender and a recipient. Status is also an antecedent of network ties because it affects individual preferences for social interaction which produce distinct forms of preferential attachment. A new generation of stochastic actor oriented models (SAOM) for social networks is now available that may help to integrate network theories and theories of networks.

As the foundation of social and economic development, infrastructure development projects are characterized by large initial investment, high technical requirements and thus generally delivered through complex contractor–subcontractor collaboration chains. This study aims to characterize the complexity of collaborative networks between contractors and subcontractors for infrastructure development through comparing the structural characteristics and the formation mechanisms of contractor–subcontractor collaborative networks for the following two different types of infrastructure: public works (PWCN) owned and operated by government agencies, and public utilities (PUCN) owned and operated by nongovernment agencies.

Based on the method of stochastic actor-oriented models and the longitudinal dataset of National Quality Award Projects in China during 2001–2020, this study compares how the structural characteristics of project-based collaborative networks between contractors and subcontractors for the two types of projects are different and how related micro-mechanisms, including both structure-based endogenous network effects and attribute-based exogenous homophily effects (institutional, organizational and geographical homophily), collectively underpin the formation of the networks.

The empirical results provide evidence that while the two networks are both characterized by relatively low levels of network density, PWCN is more globally connected around a minority of superconnected contractors as compared with PUCN. The results further reveal that compared with PUCN, the formation of PWCN is more significantly related to the structure-based anti in-isolates effect, suggesting that PWCN is more open for new entrant subcontractors. With regard to the attribute-based homophily effects, the results provide evidence that while both significantly and positively related to the effects of organizational (same company group) and geographical homophily (same location), the formation of PWCN and PUCN is oppositely driven by the institutional homophily effect (same ownership type).

As an exploratory effort of using network perspective to investigate the formation mechanisms of contractor–subcontractor relationships in the infrastructure development domain, this study contributes to a network and self-organizing system view of how contractors select subcontractors in different types of infrastructure projects. The study also provides insights into how contractor–subcontractor collaborative relationships can be better manipulated to promote the development of complex infrastructure in different contexts.

This study aims to investigate the dynamics of knowledge sharing in health care, exploring some of the factors that are more likely to influence the evolution of idea sharing and advice seeking in health care.

The authors engaged 50 pediatricians representing many subspecialties at a mid-size US children’s hospital using a social network survey to map and measure advice seeking and idea sharing networks. Through the application of Stochastic Actor-Oriented Models, the authors compared the structure of the two networks prior to a leadership program and eight weeks post conclusion.

The models indicate that health-care professionals carefully and intentionally choose with whom they share ideas and from whom to seek advice. The process is fluid, non-hierarchical and open to changing partners. Significant transitivity effects indicate that the processes of knowledge sharing can be supported by mediation and brokerage.

Hospital administrators can use this method to assess knowledge-sharing dynamics, design and evaluate professional development initiatives and promote new organizational structures that break down communication silos. This work contributes to the literature on knowledge sharing in health care by adopting a social network approach, going beyond the dyadic level and assessing the indirect influence of peers’ relationships on individual networks.

This study aims to investigate how the home country institutional development influences the alliance formation process.

A network of strategic alliances between 95 airlines over a 5-year period is analyzed with stochastic actor-oriented models [i.e. Simulation investigation for empirical network analysis (SIENA)]. Robustness analyses use a subsample of these airlines over a period of 10 years.

The results demonstrate that the membership in a firm group and a high share of state ownership are more beneficial for the number of alliances if the firm originates from a country with low institutional development.

Firms from less developed countries can use affiliations (e.g. to firm groups or the government) as signals to attract international alliance partners.

Policymakers from less developed countries should support the development of (local) firm groups to stimulate interorganizational cooperation.

Firms form alliances based on two aspects: preferences for alliance partners and attractiveness to potential partners. Prior studies outlined that institutional development affects the preferences of firms for alliance partners. This study demonstrates how the institutional development influences the attractiveness to potential partners.

When considered as organized solutions to problems of provision of public goods, Free/Open Source Software (F/OSS) productions share a number of their defining features with the organized anarchies described by Cohen, March and Olsen in their “Garbage Can Model” (GCM). The open and voluntary contribution of software developers creates constant fluctuations in levels of attention and an extremely fluid participation. The lack of predefined hierarchical access to organizational problems determines a fundamental uncertainty about how collective goals may be linked to individual activities, and in how responsibilities and tasks may be allocated efficiently within the project. Finally, the complexity involved in the collective production of tens of thousands of lines of computer code without explicit coordination creates a situation of technological ambiguity supported by a radically decentralized activity of organizational problem finding and problem solving. In this paper we take these broad similarities as point of departure to specify an empirical model that captures some of the garbage can properties of organizational problem-solving activities in the context of a specific F/OSS project followed throughout a complete release cycle. We examine the interconnected system of individual decisions emerging from problem-solving activities performed by the 135 contributors involved in the F/OSS project on the 719 software bugs reported during the period of observation. We treat the evolving two-mode network produced by encounters between carriers of organizational solutions (contributors) and organizational problems (software bugs) as a dynamic opportunity structure that constrains and enables organizational decision making. We document how stable local configurations linking problems and solutions are induced by – and at the same time sustain – decentralized problem-solving activities with meaningful self-organizing properties.

Group interaction networks are networks whose relations are defined by who engages with whom in communication, coordination, or other forms of joint activity. These interaction networks represent the patterns of action that unfold between members of a group and have the potential to inform research on how groups communicate, how psychological states inform, and how communication patterns can impact team performance. Interaction networks are unique in that they can be defined in terms of both structure and temporality; each interaction can be coded as an event that occurs at a specific point in time. Accordingly, interaction networks are well suited for process theories and methods. Further, the growing availability of fine-grained digital trace data makes it easier for researchers to study these networks in depth. In this chapter, theories of structure and time are reviewed in relation to group networks and interactions. A process-oriented relational event-based paradigm for studying group interaction networks is introduced as a possible alternative to prior methods. The chapter concludes with a comparison of relevant social network approaches, as well as a discussion of potential future research.

Purpose – Criminal groups have long been central to explanations of crime and deviance. Yet, challenges in measuring their dynamic and transient nature meant that group-level explanations were often displaced in favor of individual-level ones. This chapter outlines how network methods provide a powerful tool for modeling the dynamic nature of criminal groups.

Approach – The chapter starts by providing a brief introduction to social network analysis, including key concepts and terminology. The chapter then focuses on the types of relational data available to study criminal groups, and how network methods can be used to delineate group boundaries. The chapter concludes by presenting a framework for understanding group dynamics from a network perspective, describing the contributions of network analysis to theories of group processes.

Findings – Network methods have provided meaningful advances to the study of group dynamics, leading scholars to revisit assumptions about the impact of group’ structure on delinquent behavior. Network studies of group dynamics have primarily focused on the cohesion–delinquency link (within-group structure) and the social contagion of conflict (between-group structure), highlighting important opportunities for the intersection of these two inquiries.

Value – Network methods provide a means to revisit and extend theories of crime and delinquency with a focus on social structure. The unique affinity between group dynamics and network methods highlights immense opportunities for expanding the knowledge of collective trajectories.

This chapter conceptualizes computational methods across three related, yet distinct approaches: (1) Social Simulation, (2) Data Science, and (3) Big Data. Group communication research is then situated and reviewed along these three lines of research. Although some areas have considerable visibility (e.g., network analysis, text mining), some areas are less visible in group communication research (e.g., Social Simulation, Big Data designs). The chapter concludes with suggestions for issues regarding reliability, validity, and ethics.

Research on organizational teamwork is increasingly highlighting the patterned nature of the relational processes (e.g., communication, backup behavior) and psychological states (e.g., trust, shared cognition) that underlie team effectiveness. However, studies of teams often rely on methodologies that do not explicitly assess the underlying patterns of relational processes and states. Social network approaches offer an appealing alternative to the typical methodologies used in team research given that network approaches provide both the theory and methodology necessary to conceptualize and investigate patterns of interactions among group members. Despite the advantages of social network approaches, many team researchers are unfamiliar with the network paradigm and its associated methodologies. The purpose of this chapter is to clarify how networks can be leveraged to answer key research questions related to the study of team functioning and effectiveness. We begin by discussing the evolution and eventual convergence of team research and network approaches. Then, we examine the current state of the literature at the intersection of teams and networks in order to identify key takeaways and remaining questions. We conclude by highlighting opportunities for the future of team network science.