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This chapter describes the complexity of large-scale disaster recovery programs in the United States, the challenges faced by these programs, and the importance of multiteam systems in overcoming these challenges.

This chapter is a case study based on the experience of the authors in hurricane recovery programs.

Multiteam systems provide the range of expertise and experience needed to implement complex large-scale disaster recovery programs. For such disasters, responsibility for recovery work needs to be divided among specialized teams with unique expertise, some of which act as checks and balances for others. Challenges facing these teams include ensuring compliance with multiple Federal and state requirements, providing sufficient training to program staff, modifying procedures in response to changing program policies, and communicating changes for all pressure to move quickly, while at the same time facing intense pressure to process applications for assistance as rapidly as possible.

This chapter provides organizations responsible for disaster recovery important information about the scope of work and challenges they are likely to face following a large-scale disaster.

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.

In this chapter, we raise awareness of the larger network in which multiteam systems (MTSs) are situated. We posit that in the complex operations conducted by military units, MTSs are not isolated entities, but rather exist in exponentially complex systems that include additional challenges for both research and practice.

An operational example involving an Army Brigade Combat Team Headquarters is presented to explain the details of the exponentially complex MTSs inherent in military operations, raise awareness about challenges that plague successful mission accomplishment, and discuss the way forward for research and practice.

The Army Brigade Combat Team Headquarters is characterized as a traditional MTS, embedded in a system of hierarchical MTSs, further embedded within a parallel structure of MTSs. Challenges inherent in these organizational structures provide direction for research and practice to address the exponentially complex meta-systems that are prevalent throughout the military.

While researchers have begun to address teams existing in larger networks, or MTSs (Mathieu, Marks, & Zaccaro, 2001), much of the existing research is based on small or isolated systems. As a result, our understanding of the meta-systems in which many of these MTSs exist is limited. This chapter provides concrete examples of an exponentially complex MTS within a military environment and highlights challenges to be addressed in both research and practice.

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.

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.

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 present a case study of coordination mechanisms employed by a multiteam system (MTS) charged in an international oil and gas company.

Qualitative method with longitudinal observation and interviewing.

The MTS, and particularly its coordination mechanism of a regular collaboration meeting, provided multiple benefits, such as access to diverse competences, capabilities for solving problems crossing team boundaries, and opportunities for developing relationships between the different teams involved. There were also observed challenges: a lack of shared understanding between the participants concerning the purpose of the main collaboration session of the MTS, different levels of insight into each other’s areas of competence, information sharing, shifting team membership, multiteam membership, time pressure, and technology-mediated communication.

Providing an illustration of the MTS concept in a real organizational setting and the role of a regular collaboration meeting as a coordination mechanism for the MTS.

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.