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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.

The purpose of this paper is to highlight a collaborative effort between academia (University of North Texas, Team Sciences) and practice (Toyota Connected (TC)). This study concentrated on current problems that had been experienced by TC: How to structure and manage multiteam systems (MTSs)?

This research study utilized a realist systematic review to address an existing problem by working collaboratively with TC and academia. This collaboration involved problem identification, the development of research questions and a full systematic review guided by the research questions.

This realist systematic review merged the literature with current practices at TC in an effort to gather evidence to support the best method of structuring and managing MTSs. The findings include a leadership structure that incorporates both shared leadership (bottom-up) and existing hierarchical structures (top-down).

The MTS models presented in this study provide new models for organizations/manufacturers/industries to use as a guide when structuring their MTSs.

This study provides an example of a collaborative research effort between practice and academia using a realist systematic review. The paper also provides some multiteam system models that could be implemented and tested in different organizations. Also, new responsibilities and roles for scrum and MTSs are presented as a new method of achieving Agile.

The purpose of this chapter is to illustrate some of the complexities and decision points that must be made while attempting to collect data on multiteam systems (MTSs).

An integration of the literature along with prior experience in collecting data on MTSs in both field and laboratory environments was used to identify a series of challenges, decision points, and mitigation strategies.

Ten challenges and corresponding decision points were identified with respect to collecting data on MTSs. Specifically, seven challenges were identified that are associated with defining the research context, two with methodology and measurement, and three related to management and analysis of the collected data. While the challenges presented here are by no means the only challenges and considerations, they are some of the more frequent ones.

Provision of a series of challenges, considerations, and decision points with regard to collecting data on MTSs. Specifically, touched upon are those challenges and corresponding decision points associated with defining the research context, methodology and measurement, and data management and analysis.

Extending a model of how teams learn, this paper aims to present a model of multiteam system (MTS) learning, comparing similarities and differences between how MTSs learn and how component teams learn. The paper describes the value of adaptive, generative and transformative learning for increasing MTS development over time.

The model proposes that environmental demands trigger adaptive, generative and transformative MTS learning, which is further increased by the MTS’s readiness to learn. Learning can happen during performance episodes and during hiatus periods between performance episodes.

Learning triggers coupled with readiness to learn and the cycle and phase of MTS process influence the learning process (adaptive, generative or transformative), which in turn influences the learning outcomes.

The study offers a number of research propositions with the idea that the model and propositions will stimulate research in this area.

This model allows MTS and component team leaders and facilitators to recognize that MTS learning is a process that is needed to help component teams work together and help the MTS as a whole perform in current and future situations, thereby improving MTS effectiveness.

Little attention has been given to the notion that MTSs learn and develop. This manuscript is the first to emphasize that MTSs learn and identify processes that can improve learning. Adaptive, generative and transformative processes describe how MTSs learn and produce changes in MTS structure and actions.

To better understand contributing factors and mediating mechanisms related to team dynamics in isolated, confined, and extreme (ICE) environments.

Literature review.

Our primary focus is on cohesion and adaptation – two critical aspects of team performance in ICE environments that have received increased attention in both the literature and funding initiatives. We begin by describing the conditions that define ICE environments and review relevant individual biological, neuropsychiatric, and environmental factors that interact with team dynamics. We then outline a unifying team cohesion framework for long-duration missions and discuss several environmental, operational, organizational, and psychosocial factors that can impact team dynamics. Finally, we end with a discussion of directions for future research and countermeasure development, emphasizing the importance of temporal dynamics, multidisciplinary integration, and novel conceptual frameworks for the inherently mixed work and social setting of long-duration missions in ICE environments.

A better understanding of team dynamics over time can contribute to success in a variety of organizational settings, including space exploration, defense and security, business, education, athletics, and social relationships.

We promote a multidisciplinary approach to team dynamics in ICE environments that incorporates dynamic biological, behavioral, psychological, and organizational factors over time.

Early writings about teamwork in healthcare emphasized that healthcare providers needed to evolve from a team of experts into an expert team. This is no longer enough. As patients, accreditation bodies, and regulators increasingly demand that care is coordinated, safe, of high quality, and efficient, it is clear that healthcare organizations increasingly must function and learn not only as expert teams but also as expert multiteam systems (MTSs).

In this chapter, we offer a portrait of the robust, and albeit complex, multiteam structures that many healthcare systems are developing in order to adapt to rapid changes in regulatory and financial pressures while simultaneously improving patient safety, quality, and performance.

The notion of continuous improvement rooted in continuous learning has been embraced as a battle cry from the boardroom to the bedside, and the MTS concept offers a meaningful lens through which we can begin to understand, study, and improve these complex organizational systems dedicated to tackling some of the most important goals of our time.

The purpose of this chapter is to highlight some of the critical multiteam system (MTS) issues that are faced in healthcare by utilizing case studies that illustrate the transition of a patient through the healthcare system and suggest a possible approach to studying these issues.

The approach taken by the authors is a case study approach, which is used to illustrate the transition of a patient through several venues in a healthcare system. This approach elucidates the MTS nature of healthcare. Moreover, a methodological explanation, social network analysis (SNA), for exploring the description and analysis of MTSs in healthcare is provided.

The case study approach provides concrete examples of the complex relationship between providers caring for a single patient. The case study describes the range of shared practice in healthcare, from collaborative care within each setting to the less obvious interdependence between teams across settings. This interdependence is necessary to deliver complex care but is also a source of potential errors during care. SNA is one tool to quantify these relationships, link them to outcomes, and establish areas for future research and quality improvement efforts.

This chapter offers a unique holistic view of the transition of a patient through a healthcare system and the interdependency of care necessary to deliver care. The authors show a methodology for assessing MTSs with a discussion of utilizing SNA. This foundation may offer promise to better understand care delivery and shape programs that can lead to improvement in care.

Spaceflight presents a unique environment in which multiteam coordination is often required for mission success. This chapter will explore the topic of multiteam systems (MTSs) and their functioning in this environment.

This chapter describes the MTS case of human spaceflight in terms of a specific subset of the system involved in current human spaceflight missions: NASA Mission Control and the NASA astronauts aboard the International Space Station. In addition to describing the system itself, this chapter describes notable advantages and disadvantages of this particular MTS, along with potential future issues in human spaceflight and research directions for use of MTSs in spaceflight.

More than 40 years of successful human spaceflight missions have demonstrated many of the benefits and drawbacks of MTSs across some of the most challenging environments faced by any teams attempting coordination. These environmental challenges include extreme distances, limited modes of communication, complex systems, novel problems, and coordination between teams from multiple countries with differing goals and priorities. The specific advantages and drawbacks of MTSs in this environment, and the impacts of the aforementioned environmental challenges, are discussed.

This chapter examines a known operational and successful MTS that operates in an environment in which many of the standard assumptions regarding teams and MTSs may not apply.

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.

The field of Human Resource Management (HRM) has long recognized the importance of interpersonal influence for employee and organizational effectiveness. HRM research and practice have focused primarily on individuals’ characteristics and behaviors as a means to understand “who” is influential in organizations, with substantially less attention paid to social networks. To reinvigorate a focus on network structures to explain interpersonal influence, the authors present a comprehensive account of how network structures enable and constrain influence within organizations. The authors begin by describing how power and status, two key determinants of individual influence in organizations, operate through different mechanisms, and delineate a range of network positions that yield power, reflect status, and/or capture realized influence. Then, the authors extend initial structural views of influence beyond the positions of individuals to consider how network structures within and between groups – capturing group social capital and/or shared leadership – enable and constrain groups’ ability to influence group members, other groups, and the broader organizational system. The authors also discuss how HRM may leverage these insights to facilitate interpersonal influence in ways that support individual, group, and organizational effectiveness.