Citation in APA format: Woolley, A. W., & Mayo, A. T. (2025). Teams. In D. T. Gilbert, S. T. Fiske, E. J. Finkel, & W. B. Mendes (Eds.), The handbook of social psychology (6th ed.). Situational Press. https://doi.org/10.70400/HXAM4508

Teams

Anita Williams Woolley, Carnegie Mellon University 

Anna T. Mayo, Carnegie Mellon University 

The field of research on teams has become increasingly interdisciplinary over time, with a diminishing presence in the departments and journals of social psychology, as noted by Levine and Moreland (1989) and Hackman and Katz (2010) in the fourth and fifth editions of The Handbook of Social Psychology. Fields in which research on teamwork is prominent or growing include management, computer science, communications, engineering, applied psychology (i.e., industrial, organizational, personnel), medicine, public policy, and education (Mathieu et al., 2017). These developments have been driven in part by the rapid rise in the use of teams in practically every area of work, as teams serve as engines of new idea development, problem-solving, and product and service delivery for organizations in every sector of society (Mathieu et al., 2019; T. A. O’Neill & Salas, 2018). Teams have also significantly impacted the production of new scientific knowledge. Scientific teams are the source of the majority of new research produced over the last several decades (National Research Council, 2015), as well as the most innovative and impactful ideas in practically every field (Wu et al., 2019; Wuchty et al., 2007b, 2007a).  

Some consider the early studies of Triplett (1898) as a foundation for teams research by illuminating how the presence of others alters an individual’s performance. A little later came the Hawthorne studies of the 1920s and 1930s, where researchers began to recognize the influence of human relations in the workplace on collective performance (Mathieu et al., 2017). Building on that work, researchers soon started asking questions about how the ways that individuals work together affect the outcomes produced, studying the influence of leaders (Lewin et al., 1939), and the role of cooperation and competition (Deutsch, 1949) as well as intergroup relations (Lippitt & Radke, 1946). The mid-20th century saw a growing focus on team performance and its influences, and experimental psychology methods were applied to identify task and team characteristics, leading to better team performance (Mathieu et al., 2017). The digital revolution (which some also refer to as the Third Industrial Revolution; Rifkin, 2011) in the latter half of the 20th century, and the subsequent Information Revolution beginning in the 1990s (Bond, 1997), have both fundamentally changed many areas of life and fueled the need for teamwork in organizations as a vehicle for integrating different sources of information to solve problems. Technology has also enabled massive changes in the size and shape of teams that exist and how they collaborate, with important implications for how researchers study them (Kerrissey et al., 2020; Mathieu et al., 2019; Park et al., 2020; Wuchty et al., 2007a).  

Chapter Structure  

This chapter picks up where others left off (i.e., Hackman & Katz, 2010; Levine & Moreland, 1989), focusing our review on how the major developments in teams research over the last three decades build on earlier foundations and discussing the more recent theoretical and methodological developments driven by advances in technology and the increasing interdisciplinarity of the field. The chapter begins by providing an overview of how the rapid increase in the use of teams accompanied by the dynamic complexity characterizing their environment has led to changes in the ways that researchers theorize about how teams function and the methods they use to study them. These changes have led some to shift focus from more traditional views of team performance to more dynamic concepts of team capability and collective intelligence. Despite these major developments in theory and research, there are common threads uniting work on team effectiveness and collective intelligence across the decades which serve as the foundations around which this chapter organizes its review of extant research. The chapter starts with the team design components of "Team Composition" and "Team Structure," which continue to be the bedrock of many phenomena studied in teams research. Although team composition has long been an area of interest to teams researchers, increased recognition of the varied effects of team diversity, along with increasingly sophisticated methods for capturing its nuances, have led team composition to continue to be a focus of a growing body of research. And team structure—made up of the roles, workflows, and inter-member status relationships within the team (Bunderson & Boumgarden, 2010; Ilgen et al., 2005)—still exerts an important influence over team behavior, even in the digital age, where many believe bureaucracy is dead. The chapter then reviews developments in research on “Team Process,” which has focused primarily on the behavioral aspects of team interaction, and which researchers are increasingly distinguishing from team "emergent states" or the collective affect and cognition that develops throughout team interaction. Team process and emergent states are commonly used explanatory mechanisms connecting the effects of various inputs to outcomes in many areas of team research. And, finally, the last section of the chapter discusses new and emerging trends in teams research, reviewing the various disciplines, methodologies, and technological developments that radically change the face of team scholarship. The chapter concludes with a case for why the field of teams research still needs social psychologists to strongly influence the interdisciplinary mix of researchers to continue developing the essential psychological foundation of teams research on which many other applied fields build.  

One final introductory note: Readers of multiple chapters of this Handbook will notice several points of intersection and distinction across chapters. A significant distinction between the discussion of group phenomena in other chapters and our discussion here is our focus on teams versus groups. There is consensus among researchers that teams are interdependent collections of individuals who share responsibility for specific outcomes (Sundstrom et al., 1990). By contrast, groups can be a crowd or collection of individuals who are strangers to each other, or perhaps connected by a shared location or temporary experience, but who are otherwise not interdependent or accountable for conducting taskwork or producing any shared products or outcome(s). Thus, our discussion of phenomena such as "diversity" or "hierarchy" is focused on those concepts and their implications for teamwork. In contrast, the discussion in other chapters of The Handbook of Social Psychology considers those topics as they generally affect groups in society (e.g., Shelton & Turetsky, 2025). That said, as discussed toward the end of this chapter, the new forms of "teams" that are emerging in some cases resemble "groups" more than traditional teams as we have defined them. Thus our attempt at making this distinction could start to break down, but we hope in most instances there will be sufficient clarity that we can limit readers’ confusion.  

TEAM CAPABILITY AND PERFORMANCE  

With the rapid expansion of technology and the information revolution, there has come a surge in the body of knowledge relevant to many problems. As Einstein (1949), among others (e.g., Jones, 2009), predicted, this has resulted in individuals developing narrower expertise and, consequently, a rise in the use of teams to integrate expertise to innovate and solve problems (Wuchty et al., 2007a). Furthermore, the size of these knowledge production teams is likewise expanding (National Research Council, 2015), fueled by the ease of connecting physically distributed members (Minton-Eversole, 2012). Although teams have been traditionally defined as a stable group of people with a clear membership boundary that share responsibility for an outcome (Hackman, 1987), increasingly we see work carried out in more complex, multi-team systems (Luciano et al., 2018; Mathieu et al., 2001), where members are part of multiple teams simultaneously (O’Leary et al., 2011) leading to many collective work contexts where team membership is both unstable and unbounded (Mayo, 2022; Wageman et al., 2012).  

As social psychologists became more deeply engaged in studying the factors that affect team performance in the middle of the 20th century, they applied the careful experimental methods of their discipline, including well-defined measures of "performance" in their studies. However, gauging teams' performance in many organizational settings has become more challenging. Sometimes, the difficulty can be due to a lack of clear and measurable outcomes attributable to a team’s work. There are also questions of whether to focus on immediate outcomes versus long-term outcomes and how to prioritize the quality of a particular product versus the capability of the team to work together over time or the impact of working with a team for individual members’ growth and well-being (Hackman, 1987). For example, should a team that succeeds at delivering a project but burns everyone out in the process be considered successful?  Relatedly, given the increasing need for teams to adapt to change, some researchers argue for the need to consider other team capabilities, such as team learning, when evaluating what constitutes successful teamwork. Building on learning curve research in individual psychology (Thorndike, 1898; Thurstone, 1919), research on team learning curves has documented considerable variation in the rate of improvement observed in different teams (Argote & Epple, 1990; Dutton & Thomas, 1984; Knott, 2008). However, other researchers argue that teams can improve performance without learning (Roloff et al., 2011), often resulting in short-term gains that can’t be sustained, leading some team learning researchers to focus on team process changes instead of performance outcomes (J. M. Wilson et al., 2007).  

Given these complexities, many have advocated adopting a broader perspective when evaluating teamwork rather than using specific performance outcomes. Instead of a performance metric, research should evaluate team capability based on indicators of team functioning as complex adaptive systems (Arrow et al., 2001; Cronin et al., 2011; Hackman, 2012; Mathieu et al., 2019). Consequently, theory and research on teams has increasingly drawn on concepts originating from other fields that consider how systems adapt to dynamic complexity, such as research on intelligence (Csaszar & Steinberger, 2022; March, 2006; Mayo & Woolley, 2021). For over a century, the ability of individual, biological, and technological systems to adapt and achieve goals in a wide range of environments has been viewed as definitional to concepts of intelligence (Legg & Hutter, 2007). Thus, conceptualizing team capability in terms of collective intelligence reflects the underlying need for the ongoing information processing and adaptation necessary for teams to operate effectively in the increasingly complex, dynamic, and information-rich environments they face (Gupta & Woolley, 2021; Mayo & Woolley, 2021; Woolley et al., 2022; Woolley & Gupta, 2024).  

Initial studies on collective intelligence in teams adopted a psychometric approach similar to the one used in early psychological research on individual intelligence, and focused on the question of whether general collective intelligence even exists—that is, whether there is an equivalent of Spearman’s g for groups: a single statistical factor that captures a group’s capability to perform a wide range of tasks (Spearman, 1904). To explore this possibility, Woolley et al. (2010) sampled several task types described in widely-used task taxonomies such as the one described by McGrath (1984; see description in section on “Team Process”). For instance, some tasks involved brainstorming, and asking teams to generate as many creative ideas as possible, whereas other tasks involved finding the correct answers to puzzles or negotiating over limited resources to find the most valuable collective solution. In a factor analysis of all the groups’ scores, Woolley et al. (2010) found that the first factor accounted for 43 percent of the variance in performance on all tasks. This is comparable to the 30–50 percent of variance typically explained by the first factor in a battery of individual cognitive tasks (Chabris, 2007) and suggests the existence of a general collective intelligence factor for groups. Woolley et al. (2010) called this factor c, by analogy to Spearman’s g factor in individual intelligence. This finding has since been replicated in several studies, and measures of a team’s collective intelligence have been used to predict performance in a range of different environments (Aggarwal et al., 2019; Engel et al., 2015; Y. J. Kim et al., 2017; Riedl et al., 2021; Woolley et al., 2022; Woolley & Aggarwal, 2020).  

However, the observation of a single general c factor for collective intelligence ran counter to prevailing views in the teams literature in the extant literature, which focused on the fundamental differences across the types of tasks teams perform and their implications for group structure and process, which implied that the same team should not perform at a consistent level across different kinds of tasks (Hackman, 1968; McGrath, 1984). Subsequent research has focused on the role of individual intelligence in collective intelligence and whether the factor structure of collective intelligence is best described by a single general factor or multiple factors (see Riedl et al., 2021, for a review). New areas of research have built on and branched from the initial psychometric approaches to measuring collective intelligence and have started developing behavioral measures of collective intelligence emergence based on digital traces of team collaboration (Mayo & Woolley, 2021; Riedl et al., 2021; Woolley et al., 2022). These methods reflect an emerging shift from the heavy reliance on self-reports and laboratory experiments (Baumeister et al., 2007; Moreland et al., 2010; Wittenbaum & Moreland, 2008) to leveraging new technological capabilities to develop more behaviorally-based and unobtrusive measures for use in both lab and field settings, along with the expanding use of computational methods to theorize about and study teamwork (see final section of this chapter on "New Trends in Research on Teams"). The ability to more easily capture unobtrusive measures of team collaboration and collective intelligence has enabled the accumulation of more evidence to advance our understanding of the contributors to and inhibitors of teamwork as well as to help build tools to facilitate collective intelligence in a broader range of settings (Kittur et al., 2011; Retelny et al., 2014; Riedl et al., 2021; S. Zhou et al., 2018). Newer methods have also enabled the field to begin to heed the calls to understand teams as complex adaptive systems and to theorize and measure team capability more dynamically via indicators of collective intelligence, such that teams who are exhibiting strength on all dimensions of collective intelligence should be more capable than other teams of handling a wide range of problems in a constantly changing, complex environment (see section on "Team Process and Emergent States" for more discussion on emergent indicators of team collective intelligence). 

Despite the growth in new, more dynamic perspectives on teams and the accompanying development of new methods for studying them, some of the foundations of team capability still rest in the essential team design inputs researchers have studied for decades—team composition and team structure. The following sections discuss new developments in these areas. 

TEAM COMPOSITION  

From its inception, social psychology has emphasized studying how the social context affects the individual. When studying teams, the team’s members are an essential part of the social context, and thus, thinking about the mix of team member attributes is essential for thinking about how the team influences individual members as well as the ways that the composition of individual differences shapes the behavior of the team as a whole.  

Team Size  

The number of team members is among the critical composition variables studied for its effects on team processes and performance (Hackman & Vidmar, 1970; Mueller, 2012). Larger groups create more demands on coordination because the number of relationships to manage expands non-linearly with each additional member [possible relationships = [n*(n-1)]/2, where n = number of members]. Larger group sizes can also limit the potential for each member to contribute (Hackman & Vidmar, 1970; Shaw, 1981). Thus, as noted in the early research on the topic, adding team members can detract from team performance because larger teams are more likely to experience both coordination and motivational costs (Steiner, 1972), the latter including, for example, issues of social loafing (Latané et al., 1979). Many scholars have sought to identify an ideal or optimal size for enhancing team performance—Hackman and Vidmar (1970) observed that student teams with four to five members yielded the optimal balance of satisfaction with work and minimal frustration with coordination; Wheelan (2009) similarly observed that work groups with three to six members were significantly more productive and exhibited higher levels of trust and commitment to the group compared to larger groups. However, even early theories were careful to underscore the role of task type and level of member interdependence necessary as significant contributing factors (Steiner, 1972), which would determine the size at which process loss might exceed process gain; more interdependence makes it harder to effectively incorporate contributions from more members. That said, more recent work suggests that the capacity for larger teams to capitalize on their more extensive resource base and mitigate process loss may be improving with new technologies that support team members’ ability to contribute simultaneously and asynchronously, as reflected in research on distributed and electronically-mediated teams (Dennis & Williams, 2005; Hashmi, 2017). In addition to the mere number of team members, though, the composition of those team members’ attributes is a crucial influence on team process and performance.  

Individual Characteristics and Social Intelligence  

For at least three-quarters of a century, scholars have studied the question of team composition in terms of combinations of team member characteristics, and have come to recognize its inherent complexity. Early work on team composition focused on member ability and the strength of particular personality characteristics (Heslin, 1964; Mann, 1959). Many of those early studies followed what some have referred to as the "traditional personnel-position fit model" (Mathieu et al., 2014), which treats each position on the team as though it is an individual job in an organization, assuming that a team will be more effective if each individual has the skills for the work they are doing. Over time, researchers have recognized the additional importance of teamwork abilities, with an initial focus on team-generic competencies that all members needed to develop (Cannon-Bowers et al., 1995) some of which are also associated with individual personality characteristics, such as agreeableness.   

Another essential individual ability closely related to general collaboration skill is social intelligence, which refers to an individual’s ability to understand the feelings, thoughts, and behaviors of persons, including oneself, in interpersonal situations and to act appropriately on that understanding (Marlowe, 1986). Research has demonstrated an association between social intelligence and a variety of group and organizational outcomes, including performance (O’Boyle et al., 2011), teamwork (Farh et al., 2012), leadership (Zaccaro et al., 1991), and negotiation (Elfenbein et al., 2007). The abilities associated with social intelligence fall into two broad categories, including “social awareness,” or the ability to accurately perceive social cues and social situations, and “social facility,” or the ability to communicate and facilitate smooth social interactions (Goleman, 2021). Notably, social awareness tends to be more strongly correlated with behavior (Boyatzis et al., 2015), including the quality of team collaboration (Riedl et al., 2021). Social perceptiveness is one widely-researched measure of social awareness. It refers to an individual’s ability to pick up on subtle, nonverbal cues and draw inferences about how others think or feel (Baron-Cohen et al., 2001). In a teamwork context, accurately perceiving and interpreting social cues allows teammates to recognize “an undercurrent of information that can serve to guide their decision-making and interpersonal interactions” (Farh et al., 2012, p. 892), facilitating interdependent work. Research shows that team members’ social perceptiveness is a significant driver of collective intelligence (Meslec & Curşeu, 2013; Riedl et al., 2021; Woolley et al., 2010) because members who have strong social perceptiveness ability help facilitate a higher level of, and more equal contribution to, team communication (Engel et al., 2014), as well as other critical collaborative processes such as the coordination of task strategy and the use of members’ knowledge and skill (Riedl et al., 2021). One of the literature's most widely validated measures of social perceptiveness is the “Reading the Mind in the Eyes Test” (RME; Baron-Cohen et al., 2001). Accumulating evidence on social perceptiveness suggests that it is a biologically-based individual difference; exposure to prenatal testosterone is negatively correlated with social perceptiveness (E. Chapman et al., 2006; Honk et al., 2013), which in part explains the findings that women are, on average, more socially perceptive than men (Baron-Cohen et al., 2001). Although debates about the role of social experience and training in improving social perception ability are ongoing (Kidd & Castano, 2013, 2017; Panero et al., 2016, 2017), whatever the relative contribution of biology and experience, the result is substantial variation in the population, similar to that observed for other individual abilities (MacCann et al., 2019) with significant consequences for how team composition can affect team collaboration. 

Team Diversity 

The dominant focus of early team composition research on individual team member characteristics overlooked the role of member interrelationships and the potential effects of the combination of members’ skills and characteristics on team processes and outcomes. In the late 20th century, with the increasing demographic diversity in the U.S. population also reflected in teams in organizations, researchers began focusing more on the impact of diversity on team collaboration (Roberson, 2019), initially concentrating on team demography (e.g., O’Reilly et al., 1989). The research revealed mixed or inconsistent effects of different forms of demographic diversity for team cohesion and performance (e.g., van Knippenberg & Mell, 2016; Williams & O’Reilly, 1998). In further exploring the reasons for the inconsistent results, scholars identified an essential distinction between "surface-level diversity," based on attributes such as race or gender that can be readily observed, and "deep-level diversity," based on underlying differences in information, perspectives, and values (Jackson et al., 1995). Surface-level diversity can fuel the social categorization processes that tend to undermine team cohesion and collaboration, at least in the short term (D. Harrison et al., 1998). By contrast, deep-level diversity can provide a rich source of information and perspectives that can be integrated to improve team performance (Bell, 2007). However, the benefits of deep-level diversity do not necessarily accrue quickly or automatically. Deep-level diversity can also breed conflict (Cronin & Weingart, 2007; Firth et al., 2015), leading many team scholars to also study the contingencies that determine diversity’s effects (Guillaume et al., 2012; van Dijk et al., 2017; van Knippenberg & Mell, 2016), along with different forms of interventions which can be used to help teams make better use of members’ knowledge, skills, and abilities (Shuffler et al., 2018; Woolley et al., 2008).  

Many of the variables in early diversity research involved differences treated as categorical, such as race or gender, as sociodemographic differences and their associated challenges were the initial differences of interest. Over time, researchers have become more nuanced in considering characteristics with different distributional properties and their implications for team interaction and performance. For some traits, such as race or gender, it made sense to examine the various categories represented and by how many team members. For other characteristics, such as expertise or organizational tenure, looking at differences in quantity across members or from lowest to highest was more meaningful (such as disparity of one group versus another, or the variety of different categories represented; D. D. Harrison & Klein, 2007). Additional nuance comes from considering how team member variation in different characteristics intersect, including forms of “vertical diversity” or variation in members' level of power and status (Bunderson & Van der Vegt, 2018). As interest in studying various forms of diversity and their intersections has grown, so have the types of variables and methods used to conceptualize and analyze team diversity. For instance, some researchers criticize extant research for relying too much on a "variable-centered approach" (Emich et al., 2022), in which researchers examine the effects of mean or aggregate values of team member attributes, such as members’ mean level of a personality trait (e.g., Barrick et al., 1998; Bell, 2007; LePine, 2003), or average task-related ability (e.g., Neuman & Wright, 1999). Some have tried to improve aggregate variable-centered approaches by examining the minimum or maximum level present in the team, or its variation (such as a measure’s standard deviation or some other dispersion metric, e.g., Barrick et al., 1998). However, even these approaches have left researchers unable to explain standard puzzles, such as when two teams are identical in mean (or maximum) levels of task expertise and extraversion, but achieve very different performance levels. Scholars have developed new techniques to investigate such puzzles, such as the "attribute alignment" analysis demonstrated by Emich et al. (2022). This technique allows a researcher to examine a question such as whether the members with the most expertise were also more extroverted on some teams, and whether the alignment of those two characteristics led to better performance (given the same mean levels of expertise and extraversion in two teams. Analytic approaches like this enable researchers to evaluate multiple attributes within and between teams simultaneously to ask more nuanced questions to better explain variation in performance. This has been particularly useful in examining when and why members’ attributes affect performance, as it is increasingly apparent that the skills of some team members have a larger influence on team outcomes than others based on other characteristics they also possess, or even their role on the team (which some refer to as the “relative contribution model;” Mathieu et al., 2014).  

Other complementary approaches to research on team composition have considered the alignment of team member attributes from a few different perspectives. One area of research has focused on the alignment of different individual characteristics among different clusters of team members, resulting in "faultlines" or hypothetical dividing lines that may split a group into subgroups (Lau & Murnighan, 1998). The alignment of multiple salient attributes often leads to one or more faultlines. For example, a prototypical example in an organizational setting would be when all of the managers from human resources are women, and all the managers in manufacturing are men. The alignment of gender and job function would create a faultline, which would become even stronger if it also turned out that each group was of a different nationality or, even worse, at a different company location. Generally, the more salient dimensions of difference align, the stronger the faultline, and stronger faultlines usually mean more significant difficulties for team collaboration (Lau & Murnighan, 2005; Meyer et al., 2014). Several open questions are the focus of ongoing research.  For instance, how salient does a dimension of difference need to be to contribute to a faultline? What influences which dimensions of difference are relevant? For example, hair color and shoe size typically don’t contribute to faultlines, but family status and religion often do, even though arguably none are relevant to most work situations. Another open question is whether faultlines are always harmful, or might help coordinate work in some settings. Additional research has identified variables that moderate these effects, such as when team members maintain pro-diversity beliefs (Homan et al., 2007) or develop strong team identification (Bezrukova et al., 2009), which can mitigate the detrimental effects of faultlines in teams. Related work demonstrates additional moderators of the impact of faultlines in a team, including other team member characteristics, the characteristics of the task the team is working on, and other facets of the work process and context (Carton & Cummings, 2012; Tasheva & Hillman, 2019).  

Complementing work on team diversity, which primarily focuses on differences between different team members, is a newer area focusing on the breadth of experiences or characteristics within particular team members, which some have referred to as "intra-personal diversity" (Bunderson & Sutcliffe, 2002). One form of intra-personal diversity develops from the breadth of knowledge, skills, abilities, and expertise an individual possesses, which some refer to as "human capital" diversity (Tasheva & Hillman, 2019). Human capital diversity can develop from experience working in multiple organizational functional areas, which Bunderson and Sutcliffe (2002) found had a beneficial effect on information sharing and performance in functionally diverse teams. In addition, whereas demographic diversity is usually conceptualized and studied at the team level, it is also the case that individuals can vary in both the depth and the breadth of their exposure to and experience with different cultures, with implications for their interpersonal behavior (Maddux et al., 2021). For instance, Lu et al. (2021) observed that leaders with a greater breadth of cultural experience developed a higher level of communication competence, making them more effective leaders in general, particularly for multinational teams (traditionally more challenging to lead). Relatedly, Jang (2017) found that, in culturally diverse teams, having more multicultural members was associated with more creativity. Jang (2017) further demonstrated that individuals with multicultural backgrounds can be helpful in culturally diverse teams by engaging in "cultural brokerage" or the facilitation of interactions between actors across cultural boundaries, even when they are "cultural outsiders" in that their cultural background does not overlap with any of the members whose interaction they are facilitating. In addition to the forms of intra-personal diversity that individuals can intentionally develop via training and experience, other forms stem from biologically based individual characteristics that may not directly result from an individual’s training or life experiences. One example is cognitive versatility, a characteristic of individuals who express strength in multiple cognitive styles. Aggarwal et al. (2022) demonstrated that teams including cognitively versatile members exhibit higher levels of social integration and lower levels of task conflict as well as better performance, which Aggarwal and colleagues theorized is due to the ability of cognitively versatile team members to create connections with and between other members of the team to enhance the quality of team information processing.  

In addition to who is on the team, the way they are organized is also a long-recognized and essential element of team design and driver of team process and performance, as well as yet another facet of teamwork and teams research that has undergone massive evolution in response to the technology-driven changes that are transforming practically all areas of study in addition to most of our daily lives. The next section will review some of the more prominent changes reflected in research on team structure.  

TEAM STRUCTURE  

The introduction to this chapter briefly defined team structure as made up of the roles, workflows, and inter-member status relationships within the team (Bunderson & Boumgarden, 2010; Ilgen et al., 2005). Expanding on that further, team structure refers to the ways in which team members are arranged or organized as a collective unit, which is formed by the team boundary (defining who is and is not a member of the team) and reflected in roles and status relationships among members (which some refer to as the horizontal and vertical relationships within the team, respectively) as well as the "workflows" which are shaped by team norms and routines (Bunderson & Boumgarden, 2010; Hackman, 1987; Hollenbeck et al., 2012; Ilgen et al., 2005). Many variations on how team structure is conceptualized and described have been presented in the literature over time, with early work on the topic starting with various attempts to classify different team types.  

Overview and Evolution of Frameworks Describing Team Structure  

Early work on team structure focused on defining taxonomies or categories of team types, which often started with focusing on the team’s task or work (Hollenbeck et al., 2012). For example, multiple frameworks included a distinction between teams working on production tasks (continuous production of a service or good) and project tasks  (time-limited generation of an output; Cohen & Bailey, 1997; De Dreu & Weingart, 2003; Sundstrom et al., 1990), whereas some also distinguished advice teams (which make recommendations to a decision maker) and action/negotiation teams (which work briefly in more unpredictable circumstances; Sundstrom et al., 1990), management teams that coordinate actions and offer direction to sub-units (Cohen & Bailey, 1997), and decision-making teams (De Dreu & Weingart, 2003). The initial assumption underlying these taxonomies was that the nature of the team’s work, or their task, should determine the structure the team should adopt, such as the roles members occupy and how they coordinate their work. Eventually, researchers came to recognize the limitations of coupling specific task types with particular team structures, as a wide variety of task types and team structures proved equally suited for a particular task or type of work. These limitations, compounded by the observation that many teams work on many different task types, led much of the field to abandon these task-centered approaches to defining team structure and to search for better alternatives.  

Alternative approaches to characterizing team structure based on team tasks included frameworks classifying teams based on temporality, such as Devine and colleagues’ (1999) distinction between ad-hoc and ongoing production or project teams. Still, other taxonomies focused more on the team’s context, with targeted consideration of different elements of team structure. For example, Hackman and Wageman (2005) considered whether the team’s work is synchronous or asynchronous, along with identifying who is primarily responsible for work outcomes—the team or individual members. The resulting 2 × 2 defined four team types, two in which individuals are mainly accountable for outcomes (surgical teams and co-acting groups) and two others in which the team usually holds responsibility (face-to-face teams and distributed teams, Hackman & Wageman, 2004). Subsequent work, to account for still other dimensions, including member roles and team context, has led to a virtual explosion of frameworks and labels for a whole host of team "types," including temporary (Valentine & Edmondson, 2015) and ad-hoc teams (Salas et al., 2008), cross-functional (Majchrzak et al., 2012) and top-management teams (Lin & Shih, 2008), and self-managing (Hackman & Oldham, 1976) and hierarchical decision-making teams (Hollenbeck et al., 1995), among others.  

In more recent attempts to integrate the range of approaches used to characterize team structure, Bunderson and Boumgarden (2010) drew on related concepts from sociology and organization theory in defining structure as "the division of labor into roles and role relations," emphasizing vertical and horizontal relationships as well as their formality. Similarly, Hollenbeck and colleagues (2012) observed that underlying the 42 different team "types" identified in the literature are three critical structural elements: a team’s skill differentiation (horizontal relationships) and hierarchical differentiation (vertical relationships), as well as the team’s temporal stability (see also Lee et al., 2015). The resulting framework—the "Team Descriptive Index" (TDI; Hollenbeck et al., 2012; Lee et al., 2015)—is a three-dimensional approach to describing teams that has been widely adopted and plays an essential role in integrating extant research on team structure. The present review draws on the TDI framework to guide the following three sections on team structure and concludes with a brief discussion of research on team norms and routines (Hackman, 1987).  

Horizontal Differentiation  

The first dimension of team structure is the team’s horizontal differentiation, captured by the various roles or specializations across members and their interdependencies (Bunderson & Boumgarden, 2010; Hollenbeck et al., 2012).  

Roles and Specialization  

One key element of team structure is how members’ skills and experiences fit together, particularly the extent to which individuals’ skill sets are similar or distinct, shaping the degree to which team members are substitutable for one another (Hollenbeck et al., 2012). Research on skill differentiation in teams has led to a focus on team roles—structures that define members’ expected responsibilities and behaviors (Bechky, 2006; Katz & Kahn, 1966)—along with whether a team’s members are specialists or generalists (e.g., Fahrenkopf et al., 2020)—two team features of particular import in light of trends toward specialization (Jones, 2009; Wuchty et al., 2007a) and role-based work (Valentine & Edmondson, 2015) across industries. Defining specific roles for team members can serve as a primary mechanism of team coordination (Okhuysen & Bechky, 2009) and facilitate efficient work (Salas et al., 2008). At the same time, there is evidence that having team members who are generalists and who maintain some redundancy in ability with others is vital to supporting flexibility and adaptation (Hollenbeck et al., 2002). Consistent with this notion, a study of SWAT teams demonstrated the importance of role-shifting for responding to surprises (Bechky & Okhuysen, 2011), and research on teams in emergency departments revealed that team members’ (i.e., nurses’ and physicians’) extra-role behaviors were critical to coordination (Valentine, 2018). Consistent findings are reported in experiments on team member turnover, such as in one study in which researchers observed that teams tended to perform better when the newcomer was a generalist versus a specialist due to the generalist’s greater likelihood of transferring helpful knowledge from a prior group (Fahrenkopf et al., 2020).  

Interdependence  

In addition to the division of responsibilities among team members, another critical feature of horizontal differentiation in team structure is the level of interdependence among team members (J. D. Thompson, 1967). Early work on interdependence distinguished its different forms based on how team members combined their inputs, such as whether their work was pooled (or combined with little interaction), sequential (or passed from one member to another), or reciprocal (where members iterate together on work products; J. D. Thompson, 1967). Later work added consideration of the nature of interdependence, distinguishing between the extent to which the task work is interdependent itself (which could occur via inputs—people and other resources—as well as work processes) versus resulting task outcomes or rewards (Wageman, 1995). More recent work in organizational theory and design has further differentiated task interdependence (i.e., what members do) from knowledge interdependence (i.e., what they know; Raveendran et al., 2020). Task interdependence maintains a close relationship with team roles; consider that when teams assign roles based on specific functions or expertise, they tend to have more narrowly specialized roles, which, in turn, requires more task interdependence compared to teams whose roles are based on geography or industry, leading members to cover more areas of expertise and develop redundant capabilities which reduce their need to rely on others (Hollenbeck et al., 2002). Some have argued that both task and reward interdependence are crucial for team effectiveness (Shea & Guzzo, 1987); subsequent work has refined that understanding, demonstrating (a) that task interdependence affects team cooperation, whereas reward interdependence affects effort (Wageman et al., 2005); and (b) the critical importance of their alignment, such that teams work in settings where task and outcome interdependence are either both high or low (Wageman, 1995).  

Furthermore, although much of the initial research on interdependence typically conceptualized it as structural or hard-wired into the design of the team’s work and/or rewards, scholars have also noted that there can be latitude in the ways that structural interdependence is realized in the work of a team, allowing for interdependence to arise as an emergent state based on the patterns of team member interactions that unfold over time (Caruso & Woolley, 2008; Mayo, 2022; Wageman et al., 2012; Wageman & Gordon, 2005). The concept of emergent interdependence is addressed later as part of the discussion of emergent states.  

Vertical Differentiation  

A second dimension of team structure addressed in different areas of teams research is the vertical differentiation of team members, or the team’s hierarchy (Greer et al., 2018; Magee & Galinsky, 2008; see also Anderson, 2025). Vertical differentiation reflects the rank order of group members, often along several attributes, which may or may not align with one another. These include authority (or decision-making rights), power (or control over resources), influence, and status (i.e., respect and admiration from others (i.e. respect from others; Hackman, 2011; Hays et al., 2022; Lee et al., 2015; Magee & Galinsky, 2008).  

There are mixed perspectives regarding when and how team hierarchy benefits team collaboration. From one view, hierarchy offers clarity about expected workflows that can enable coordination and team performance (e.g., Halevy et al., 2011, 2012) as well as team learning (Bunderson & Boumgarden, 2010), mainly when a team’s work is high in interdependence (Halevy et al., 2011). The behavior of the leader is also important, as research has shown that having leaders who ensure that lower-ranking members have a voice and engage in inclusive leadership (Nembhard & Edmondson, 2006) and information management (Larson, 2010; Larson et al., 1996) results in functional hierarchies that enable effective information use. This view aligns with classic research at the organizational level, which touts organizational structures as critical for directing attention and facilitating the coordination of work (March & Simon, 1958). However, other studies suggest that hierarchy is more likely to dampen the voice of lower-ranking members and create conflict, ultimately detracting from team performance (Greer et al., 2017, 2018) as well as negatively affecting inter-team interactions (Doyle et al., 2022). To reconcile these views, studies have pointed to some contingencies influencing when hierarchy is beneficial and when it is not.  

One contingency influencing whether hierarchy is beneficial or not relates to the basis for the differentiation of members and whether everyone agrees on who is in what position (Doyle et al., 2022; Hays et al., 2022; Magee & Galinsky, 2008; Yu et al., 2019). Hierarchy is most beneficial when the different bases of rank-ordering members are aligned (such as when those with more power also have more status Halevy et al., 2011) or at least are not misaligned (e.g., members have different amounts of formal authority but are not differentiated in status). In contrast, misalignment can harm group functioning via conflict and competition. Other research has focused on the effects of different attributes of the hierarchy, such as how centralized it is (i.e., power is concentrated in one or a few members) or the steepness of the hierarchy (i.e. the difference in power between levels; Wellman et al., 2020). For example, one study demonstrated that the centralization and steepness in different levels of influence team members possessed were associated with more team conflict and worse team performance and satisfaction (Bunderson et al., 2016). However, the "acyclicity" of influence, or the degree to which influence is uni-directional such that members never have direct or indirect influence over someone who influences them, was associated with less conflict and better team performance and satisfaction (Bunderson et al., 2016). This is an example of the type of nuance researchers are uncovering as among the conditions under which hierarchy will have negative or positive implications for the team.  

A second important contingency influencing the effects of team hierarchy relates to the hierarchy’s level of stability or the possibility for the hierarchy to change (Tajfel & Turner, 1979). A widely supported conclusion is that the negative effect of team hierarchy on team interaction and performance is exacerbated when the hierarchy is mutable or unstable (Greer et al., 2018). However, an important caveat appears to be the basis on which team members can gain status when a hierarchy is unstable; if members can gain status by making essential contributions to improve the team’s work, such as by sharing critical information, then unstable status hierarchies can lead members to engage in more knowledge sharing (to gain status), which enhances team interaction and performance, mainly if there is knowledge diversity in the team (Gray et al., 2022). In knowledge-diverse teams, hierarchy instability may reflect shifts in the deference to members’ differentiated expertise, as reflected by research demonstrating the value of dynamic power structures (Satterstrom, 2016), particularly in cross-functional teams (Aime et al., 2014). Flexible or dynamic hierarchies are also typical in teams adopting shared leadership structures, which two meta-analyses have demonstrated is associated with team effectiveness (D’Innocenzo et al., 2016; D. Wang et al., 2014), particularly in field settings (D’Innocenzo et al., 2016) where team members are more likely to be horizontally differentiated based on expertise or functional area. Taken together, these findings underscore the emerging consensus that dynamic or unstable hierarchies may be beneficial when the changes to the hierarchy are driven by changes in members’ relative ability to provide critical resources to enhance team interaction and performance, which leads members to see the changes that occur as legitimate (Aime et al., 2014; Halevy et al., 2011). However, when members’ status within the group is not predicated on their ability to help the group achieve its goals but rather on some other basis, then unstable hierarchies can lead members to engage in behavior that harms group performance, such as undermining other group members or their propensity to cooperate with others, in their efforts to win power or status (Bendersky & Hays, 2012; Bendersky & Pai, 2018; Case & Maner, 2014; Gray et al., 2022; Hays & Bendersky, 2015).  

An additional contingency shaping the conditions under which unstable hierarchy contributes to or detracts from team performance is the gender composition of the team. In a laboratory study, Woolley and colleagues (2022) observed that, consistent with extant work, stable leadership hierarchies generated more cooperative patterns of interaction (i.e., more interactional synchrony) relative to unstable hierarchies, whereas unstable hierarchies generated more competitive interactions (i.e., more verbal interruptions during team collaboration). However, the effect of more cooperative or competitive interaction patterns was contingent on the team’s gender composition; more cooperative interactions led to higher collective intelligence in majority-female teams, whereas more competitive interactions led to higher collective intelligence in all-male teams. On further analysis, the researchers noted that the majority-female teams exhibited higher effort and engagement when the team interaction was cooperative rather than competitive, which is consistent with the types of interaction patterns that are seen as more normative in related research in economics and psychology (e.g., Eagly, 1987; Niederle & Vesterlund, 2011). By that same token, more competitive interactions are more normative in all-male teams and are associated with higher engagement levels.  

Extant research illustrates that team hierarchy is an influential aspect of team structure with essential implications for team processes and performance. Still, its potential benefits may hinge on additional factors, such as the nature of the hierarchy, the characteristics of the team’s work, and the team’s composition with respect to knowledge diversity and gender, to name a few considerations, which influence the behaviors members can engage in to gain status and the interaction patterns that are viewed as normative.  

Temporal Stability

A third feature of team structure examined by frameworks such as the TDI (Hollenbeck et al., 2012) is the team’s temporal stability, which includes the team’s longevity as well as the stability of the team’s membership and the clarity of the team’s boundary. A team’s longevity can create opportunities for members to learn how to work together (e.g., Liang et al., 1995) and to overcome challenges that may be present initially, such as the common challenges faced when working in a diverse group (Pelled et al., 1999; Schippers et al., 2003; Zhang, 2016). In contrast to long-term teams, temporary teams lack the formal structure of stability, which seems to make other team structures, such as roles and boundaries, particularly important for enabling coordination (Bechky, 2006; K. J. Klein et al., 2006; Valentine & Edmondson, 2015) as well as other helpful team qualities such as "swift trust" (Meyerson et al., 1996).  

Membership stability is another form of stability in a team structure that could be accompanied by team temporal stability or longevity, but the two are orthogonal; a team with long-standing temporal stability could still experience high rates of membership change, whereas another shorter-term or temporary team could maintain stable membership throughout its brief team lifespan. A robust body of work has explored the impacts of membership stability, often by documenting the impact of instability, or changes in membership. Team membership change can be adding a newcomer, losing a member, and/or replacing a member. In some cases, adding a new member, either by increasing team size or replacing a current member, can be beneficial, mainly when the newcomer introduces new and valuable ideas to incumbents who are willing to adopt them (H. S. Choi & Thompson, 2005; Fahrenkopf et al., 2020; Kane et al., 2005; Kane, 2010; Kane & Rink, 2015, 2016). However, most instances of membership change reduce team performance, partly due to disruptions to the team’s transactive memory system (TMS; Christian et al., 2014; Lewis et al., 2007; see section on team emergent states for further discussion of TMS). Scholars have also focused on issues related to the turnover of specific team roles, noting that member replacement is particularly disruptive when more central or core members are replaced because of more significant disruption to the team’s coordination (Humphrey et al., 2009; Stuart, 2017; Summers et al., 2012).  

Another feature of team structure closely related to both temporal and membership stability is the clarity or strength of a team’s boundary (Mortensen & Haas, 2018), distinguishing who is a team member from who is not. The notion of teams being "bounded" is so fundamental to team scholarship that for several decades, it was treated by some scholars as definitional to the existence of a team (e.g., Hackman, 1987). Clear boundaries have been shown to facilitate coordination and ensure accountability (Mortensen & Hinds, 2002; Wageman et al., 2005, 2012). Despite the advantages of a clear boundary, the reality of work in modern organizations is that boundaries are not always clear or stable, resulting in what some have called "sand dune teams" (Hackman & Wageman, 2005, p. 56), and this can make it difficult to pin down who is in the team at any given moment (Wageman et al., 2012), which is negatively associated with team performance (Mortensen, 2014; Mortensen & Hinds, 2002). In cases when a team’s boundary is fuzzy, clarifying it through physical markers (Valentine & Edmondson, 2015) or other interventions (as described in the next section on "Team Starts") can enable coordination and team effectiveness. These issues are addressed later when discussing emerging trends related to "dynamic teams."  

Team Norms and Routines  

How norms form in groups and influence behavior has been of interest to social psychologists for most of the field's history (e.g., Allport, 1920; Sherif, 1936). How team norms (Bettenhausen & Murnighan, 1985; Feldman, 1984; Levine et al., 2000) and routines (Gersick & Hackman, 1990) are created has been the topic of substantial research for several decades. Research demonstrates that norms and routines can be shaped by several inputs, including shared training (Cannon-Bowers et al., 1995; Liang et al., 1995; Salas et al., 2008), during which team members could be trained in a manner that establishes norms or routines before group work begins, or similarity in professional background which leads members to come together with shared expectations for their work and the rapid development of shared norms and routines (Bettenhausen & Murnighan, 1985; Gersick & Hackman, 1990). Whatever the specific source or influence, extant work consistently demonstrates that team norms can be most quickly established when groups first convene, as members look to each other’s behavior to understand what is appropriate or expected (Bettenhausen & Murnighan, 1985; Feldman, 1984). Alternatively, leaders can explicitly shape norms via interventions (Hackman & Wageman, 2004; Mathieu & Rapp, 2009; Woolley et al., 2008) or group planning (Janicik & Bartel, 2003), during which productive norms can be more explicitly established. Further, even non-leaders can affect team norms; for instance, a study with student teams found that when either the leader or non-leader team members began their work together with the expectation of engaging in a high level of collaborative problem-solving, teams developed a clearer norm of engaging in more collaborative problem-solving (Taggar & Ellis, 2007). Once established, norms and routines tend to persist due to inertia, and teams have a tendency not to change behavioral patterns unless the group experiences a crisis, or another unexpected event prompts team members to revisit their established and expected patterns of work (Gersick & Hackman, 1990; Hackman & Wageman, 2004).  

One way that leaders and organizations can more intentionally shape productive norms in teams is via the use of "team starts" (also referred to as “team launches,” Ginnett, 2010). In many team settings, particularly those where there is some similarity in the work members do across teams (such as airline crews, medical teams, and professional service teams, among others), team members start with a provisional structure or team "shell," consisting of a general set of member roles and workflows that have been established based on a general outline of how most teams operate. In a new team’s initial gathering, they can elaborate and clarify many of the details, including the team’s boundary, member roles, hierarchy, expected workflows, and team norms, which "breathes life into the shell," allowing for a shared understanding of what to do and how to do it, along with motivation to do so (Ginnett, 2010, p. 105). Indeed, a growing body of evidence supports the positive impact of team "launches" of various forms (Ericksen & Dyer, 2004; Ginnett, 2010; Hackman, 2011; Mathieu & Rapp, 2009; Woolley, 2009). More specifically, research demonstrates that team launches can clarify both “taskwork,” or what the team is doing, and “teamwork,” or how the team members will do that taskwork with each other (Marks et al., 2001), which have been shown to jointly enhance subsequent team processes and performance (Mathieu & Rapp, 2009). Of course, as noted above, team structures can also emerge and evolve—at least to some degree. However, teams also exhibit a fair amount of inertia, suggesting a limit to how much they can change (Gersick & Hackman, 1990). Thus, more research is needed on the dynamics of these structures and how to support their emergence and evolution beyond the early moments of a team’s life.  

As noted in the introduction to this chapter, team composition and structure are two crucial foundational components that set the stage for team process and emergent states—two rapidly growing areas of the field in terms of research topics and methodological advances, which are reviewed next. 

TEAM PROCESS AND EMERGENT STATES  

Much of the research on teams has been guided by the "Input-Process-Output" or IPO framework (Ilgen et al., 2005). This framework positions team process as resulting from inputs (such as team composition and structure) and flowing linearly to affect outputs. In reality, teams are complex adaptive systems (Arrow et al., 2001; Cronin et al., 2011; Mathieu et al., 2019). The team process is not only influenced by inputs but also influences composition and structure in a reciprocal relationship. In considering the various forms that the team process can take, an important distinction that has come to be more widely appreciated is the differentiation of the team process from "emergent states" (Cohen & Bailey, 1997; Marks et al., 2005; Rapp et al., 2021). Traditional research on team process has focused primarily on the behavioral aspects of team interaction, whereas team emergent states typically encompass collective affect and cognition. Although discussed as distinct categories, team process and emergent states are closely interrelated, so there is not always consensus on when a construct falls into one category (Marks et al., 2001; Rapp et al., 2021). This section reviews some of the key frameworks and findings in research on team process and emergent states.  

Team Process  

Team process refers to how members achieve collective goals by combining their inputs and the associated behavior, communication, and cognition (Marks et al., 2001). Team process is thus a key driver of coordination (Faraj & Xiao, 2006; Okhuysen & Bechky, 2009).  

A classic approach that many researchers use in thinking about the role of team process in team performance is represented by Steiner’s equation: Team productivity = potential productivity – process loss (Steiner, 1966). Steiner’s initial focus was on the role of faulty group process in reducing group productivity. The “potential” element of this equation captures what is possible given the individual abilities of the group’s members—the group's performance if all members’ contributions were delivered at their peak level effort and coordinated perfectly to avoid any process loss (L. Thompson, 2008). Other researchers came along later to add a slightly more optimistic spin by considering the potential for process gain (Hackman & Morris, 1975; Hill, 1982; Shaw & Ashton, 1976), eventually amending Steiner’s equation to include it: Team productivity = team potential + process gain – process loss. Process gains refer to the potential for synergies, such as with knowledge integration that goes beyond the simple aggregation of different knowledge to create new knowledge (e.g., Hackman & Morris, 1975). This addition of process gains to the equation opened the door to modeling synergy as occurring when process gains exceed process losses and the team thus outperforms its potential, which here is defined by a simple aggregation of team member capabilities (Larson, 2010).  

Scholars have proposed a variety of frameworks for characterizing different categories of team process. A classic distinction in the management literature is between task-oriented processes (i.e., to manage taskwork) versus interpersonal processes (i.e. to manage relationships Devine, 2002; Fleishman et al., 1991). Similarly, some team process frameworks distinguish between task and interpersonal process, such as Marks and colleagues (2001), who differentiated two types of task-related process (i.e., “transition” and “action,” which involve planning, coordinating, and monitoring taskwork) from interpersonal processes (such as conflict management, managing emotion, and motivation).  

Another common way that team researchers have characterized different types of team processes is based on the team’s task and the ways that members need to combine their contributions—their process—to effectively accomplish tasks of different types. For instance, Steiner (1966) identified tasks as (a) disjunctive (where performance is determined by the best member, e.g., answering a math question), versus conjunctive (where performance is determined by the worst member, e.g., a relay race) and (b) additive tasks (where performance is determined by the sum of its members’ independent work, e.g., shoveling snow) versus compensatory (members’ errors offset one another, such as predicting the future value of something) or complementary (where members can combine resources to achieve synergistic gains). Steiner (1972)  and later Laughlin (1980) also distinguished tasks with a demonstrably correct solution (which Laughlin referred to as “intellective”) from those based on judgment. Another consideration was the level of interdependence of members' inputs, which Steiner handled by classifying tasks as divisible (or able to be divided among team members) versus unitary (or not easily subdivided.)     

McGrath's (1984) Group Task Circumplex integrated interdependence into four categories of task “types” describing what groups do: Generate (as in brainstorming), Choose, Negotiate, or Execute with implications for how interdependently members need to work to combine their inputs. Generate tasks can often be completed with members working at a relatively low level of interdependence by simply pooling ideas or inputs. Choose requires more integration to determine the correct answer or consensus judgment (although that can be automated, as in polling a crowd). Negotiate tasks usually require high interdependence as members reason about their individual and collective goals and devise solutions. Execute tasks can vary more in interdependence depending on the necessary level of coordination of members’ inputs. McGrath further distinguished tasks that are cooperative from those that are competitive or mixed-motive. For instance, Generate tasks are typically cooperative, but Negotiate tasks are mostly competitive or mixed-motive. By contrast, an Execute task can be cooperative, such as performing a dance, or competitive, as in a sports contest or a battle. These distinctions affect the team processes likely to develop to support effective task performance.  

Research on team process has developed across many different areas, in several cases spilling over into new disciplines (such as communications) or becoming new subfields of teams research in and of themselves. Here, the chapter discusses team process research as it has developed through research on communications, then reviews developments in some of the more active areas of team process research focused on team decision-making, creativity, conflict, and learning. The ensuing section discusses a more recent and growing body of research demonstrating the importance of "external activities" in the team process. This area has long been overlooked in the literature.  

Communication  

Team communication is a fundamental building block for many other elements of team process, as reflected in the many references to communication when describing team decision-making, creativity, and a host of other team processes. A whole field of communications research examines the process of interpersonal conversation and its influences on social interactions and relationships (see Cooney & Wheatley, 2025). Teams scholars have built on this work in examining a host of variables that influence team communication, as well as the ways that communication both influences and serves as an indicator of the quality of team processes or emergent states.  

Many of the team composition and structural inputs discussed in previous sections of this chapter affect the team process in part by influencing team communication. For example, team composition characteristics such as cultural diversity and gender composition or individual member characteristics such as personality and social intelligence are among the team composition variables that influence how much a team communicates or how equally different members participate in the conversation (Chikersal et al., 2017; Engel et al., 2014; Haan et al., 2021; Woolley et al., 2010). Communication affects team performance by influencing whether a team can benefit from members’ expertise. Research on expertise use in diverse teams has yielded mixed results. In some cases, team diversity has a negative effect on expertise use (Joshi, 2014; Joshi & Knight, 2015), whereas in other cases, it has a positive effect (e.g., Loyd et al., 2013; Phillips & Loyd, 2006; Thomas-Hunt & Phillips, 2004). One reason is related to team communication: Experts often tend to talk more than non-experts, and when they do, team performance is better (Bottger & Yetton, 1988; Yetton & Bottger, 1982). Yet, in some circumstances, experts do not participate more than non-experts, and studies show that is more likely to happen when team members with more expertise are in the minority (Mayo et al., 2020). Social categorization processes can lead members in the minority (i.e., individuals who are dissimilar to the rest of the team on a salient attribute, such as being female in a male-dominated team) to be less committed to or identified with the team, and thus share less task information (Guillaume et al., 2012; Shemla et al., 2016) compared to majority members with the same level of expertise. Furthermore, team performance is enhanced when experts’ opinions are more influential (Baumann & Bonner, 2004; Libby et al., 1987; Rogelberg et al., 1992). Yet, it is often the case that expert team members are not the most influential (e.g., Bonner & Sillito, 2011) because, assuming a team member has spoken, that team member’s influence is determined by others’ reactions to that contribution (Bonner & Baumann, 2012; Littlepage et al., 1997). However, here is where there is some evidence for an advantage for team diversity, in that some studies suggest that individuals from the minority are likely to elicit greater attention from other team members (Thomas-Hunt et al., 2003) and to elicit the expectation for, and receptiveness to, divergent perspectives because they represent a different category (Phillips, 2003; Phillips & Loyd, 2006; Sommers, 2006; Thomas-Hunt et al., 2003). Consistent with this possibility, Phillips and colleagues (2004) found that when minority team members shared their unique knowledge, the knowledge was repeated more, and the teams made better decisions than if that same knowledge was shared by a majority team member, indicating that knowledge from a minority team member was more influential.  

In addition to communication being a vital team process that is instrumental to team performance, researchers also use communication patterns to indicate the quality of other team processes or emergent states. For instance, researchers have regularly observed that in most collaborating teams, various attributes of speech become more similar across members over time as a result of entrainment processes (or the convergence of speech rate, pitch, prosody; Wynn & Borrie, 2020) or interactional synchrony (Jasnow et al., 1988) where interaction partners look to each other for cues for when to talk, how much to speak, and when to remain quiet to minimize the likelihood of simultaneous starts (M. Wilson & Wilson, 2005). Doing so requires highly coordinated timing, during which participants attend closely to one another’s cues, automatically adapting to each other’s communicative behaviors by adjustments in intonation, tempo, frequency, and duration (Giles et al., 1991). Though most normal social interactions are characterized by some level of interactional synchrony, there is also variation in the degree to which interactional synchrony develops, which some researchers interpret as a signal of the quality of team interaction (e.g., Chikersal et al., 2017; Tomprou et al., 2021; Woolley et al., 2022).  

Team Process and Decision-Making  

Team decision-making has been an area of interest to researchers for several decades. As teamwork becomes increasingly knowledge-intensive, the importance of team decision-making and the related information processing required for teams to make good decisions continues to grow. Alongside the growth in the amount of decision-making handled by teams is a large body of evidence showing how teams frequently base their decisions on irrelevant information and disregard relevant information (Larson, 2010). Due to various cognitive and motivational factors, the main problems experienced in team decision-making are associated with surfacing the necessary and pertinent information and combining it appropriately. The cognitive hindrances stem from many of the individual biases that have been well-documented in research on individual decision-making; when examining those biases in teams, the tendency in teams whose members are prone to the same personal biases is to mistake the resulting appearance of consensus for accuracy (Kahneman et al., 2021). Individual biases can also be amplified in team settings, such as with an established tendency for teams to be even more prone to escalate commitment (i.e., increasing investment into a failing project) than individual decision-makers (Kerr et al., 1996; Whyte, 1993). Similarly, work on social decision schemes has investigated how pre-decision preferences combine into joint decisions (Davis, 1973). In particular, teams are vulnerable to the initial distribution of information and the existence of “hidden profiles,” in which members initially prefer different alternatives based on conflicting information they hold, necessitating the surfacing and sharing of all relevant information to reach the correct solution (Stasser & Titus, 1985). In addition to these cognitive factors, motivational or reputational phenomena can negatively affect team decision-making. Although initial work by Janis (1982) on groupthink has received mixed support in the literature (Baron, 2005; Turner & Pratkanis, 1998), related research has demonstrated that teams are at risk of polarization, wherein initial views become more extreme through team interaction (Isenberg, 1986; Schkade et al., 2010). The risk of team polarization is also increased by conformity pressures and cascades that prevent divergent perspectives from surfacing because team members with minority viewpoints want to be accepted by the other team members (or fear the reputational consequences of being wrong) (Asch, 1951; Deutsch & Gerard, 1955; Sunstein & Hastie, 2015). 

Approaches to improving team decision-making focus on team members’ motivation to overlook disconfirming evidence and to believe in the infallibility of their team, with mitigation strategies examined in work on group polarization as well as in social comparison approaches to team decision-making (Isenberg, 1986; Janis & Mann, 1977; Sanders & Baron, 1977). Toma and Butera (2009) further demonstrated how motivation alters team cognition, observing that within-team competition leads team members to share less information and to be less willing to consider information that disconfirms their initial preferences, as a result of mistrusting their teammates. In combining social and epistemic motivational factors, De Dreu et al. (2008) proposed a theory of motivated information processing in teams, in which epistemic motivation determines how deep versus shallow team members will be in seeking out information, whereas social motivation encompasses the mixed cooperation-competition motives that team members experience which determine the nature of the information that is shared and processed by the team. Epistemic and social motivations interact to shape the quality of team judgment and decision-making.  

When groups avoid these information processing pitfalls described, process gains in the form of cognitive synergy are possible, reflected in the objective gain in group performance, relative to individual member performance attributable to group interaction (Larson, 2010). Whereas weak synergy denotes group performance that is better than its average member performance, strong synergy refers to group performance that exceeds the best member’s performance (Larson, 2007, 2010; Meslec & Curşeu, 2013). This notion of cognitive synergy is similar to what Collins and Guetzkow (1964) termed the assembly bonus effect or group performance beyond the additive effects of individual members. As such, cognitive synergy is directly attributable to the information-processing capability of the team, and interventions to improve cognitive synergy often focus on the decision rules that teams use for combining members’ inputs (Davis, 1973; Meslec et al., 2014; Meslec & Curşeu, 2013). Achieving strong cognitive synergy depends fundamentally on identifying member expertise, combining the expertise held by individual members, and employing effective processes to use this expertise to outperform the best individual member, which are essential elements of group information processing (Hinsz et al., 1997).  

Given the biases and difficulties documented with team decision-making in some circumstances, some researchers have explored the use of collections of independent decision-makers, or “crowds,” to gain the advantage of multiple perspectives without the drawbacks of the social processes that bias decisions. First demonstrated by Galton (1907), it has since been shown repeatedly in studies of guessing and problem-solving that the average of many individuals’ independent estimates can be closer to the actual value than all of the separate individual or even expert guesses. However, for any benefits to accrue from the use of a crowd, individuals must be completely independent of one another, and the sample sufficiently large and unbiased to enable errors to be equally distributed (Becker et al., 2017; Larrick et al., 2012; Surowiecki, 2004). Even subtle social influence revealing knowledge of others’ estimates can create a cascade of effects that reduces the accuracy of crowds (Lorenz et al., 2011).  

Although crowds can be helpful for some types of decisions when the conditions for accuracy are in place, there is a range of other circumstances, such as when the options are not well-defined or when there needs to be buy-in by the team for a decision to be implemented, when decisions are better made by interacting teams. In these circumstances, several interventions have been demonstrated to successfully improve team decision-making, typically by intervening to alter team process. One type of intervention focuses on structuring team conversation so that the team identifies key goals or questions that need to be answered and how their information needs to be integrated to answer those questions (Woolley et al., 2008). This approach can also be operationalized as deliberation maps in online communication, in which members contribute information in a structured format to facilitate the organization of ideas and the balanced consideration of different options (M. Klein, 2021). A second type of intervention in team decision-making emphasizes the motivation to search for, and openness to, unique and disconfirming evidence. For instance, team members might be assigned to different roles such that it is clear that each person brings something unique to the table (Stewart & Stasser, 1995), or there might be an emphasis on the value of having diversity in the team (Homan et al., 2007). Another variation involves assigning team members to adopt opposing points of view. A common form is the “devil’s advocate” approach, named after a similar process adopted during the 16th Century as part of the canonization process in the Roman Catholic Church, in which an appointed person adopts a skeptical perspective on a candidate, in opposition to "God’s advocate," who argued in favor. Better still is inviting genuine dissent from team members (Schulz-Hardt et al., 2002).  

A third approach to improving the quality of team decision-making involves encouraging a team to grant equal speaking time to all team members, assuming that this will enable all relevant facts to be brought into the discussion. Equality in speaking time has been associated with higher collective intelligence in teams (Engel et al., 2014; Woolley et al., 2010). Interventions involving real-time feedback on relative contributions to team conversation have been shown to improve team decision-making performance (DiMicco et al., 2007; Glikson et al., 2019) although some studies do demonstrate that teams do even better when there is also a facilitator or a structured method for ensuring that the decision-making criteria are clear and that all relevant information is shared (Dalkey & Helmer, 1963; DeSanctis & Gallupe, 1987; Woolley et al., 2008).  

Team Process and Creativity  

Another substantial body of team process research has been dedicated to understanding how teams might capitalize on their available skills and talents to produce something creative—something novel and useful (Amabile, 1982, 1983). Team creativity has been measured in various ways, including ratings of a product or solution’s novelty or its novelty and usefulness, the number of ideas generated, and the number of distinct categories of ideas generated (J. Zhou & Hoever, 2014). For some, team creativity focuses on the outcome, such as innovation—the later stage of work on the implementation of a new idea (Reiter-Palmon et al., 2012).  

Comparing team and individual creativity, research has identified ways that teams are ineffective at different process stages. For example, teams can underperform when attempting to generate a large number of creative ideas, often falling short of the achievements of their members working independently (e.g., Diehl & Stroebe, 1987, 1991; Johnson & D’Lauro, 2018; Stroebe & Diehl, 1994). Yet, teams also have the possibility of cross-stimulation, or inspire ideas that no individual would have developed alone, which makes it possible for teams to outperform the sum of their parts (Dugosh et al., 2000; Stroebe et al., 2010; Tindale & Larson, 1992). Thus, some research has examined how team composition influences creative outcomes (e.g., van Knippenberg & Schippers, 2007), or how structural interventions common to real-world contexts can improve team creativity (e.g., Korde & Paulus, 2017), including the team processes by which creative ideas emerge in teams (J. Zhou & Hoever, 2014). Relatedly, Taggar (2002) found that "team creativity-relevant processes," including communication and conflict management, were vital in capitalizing on the potential of a team’s individuals. The most widely studied process related to team creativity, though, has been idea generation and brainstorming (Reiter-Palmon et al., 2012).  

Paulus and Dzindolet’s (2008) team creativity model describes cognitive and motivational pathways whereby team members can influence individual and collective creative performance. Process gain occurs via cognitive pathways through dynamics such as cross-stimulation, where an idea from one member inspires a new idea in a different member. Process gain via motivational pathways can take the form of social facilitation, where members apply more effort to match the level of their teammates (Zajonc, 1965).  

The Search for Ideas in Associative Memory (SIAM) model, articulated by Nijstad and colleagues (2002), theorized that mental images trigger idea archetypes representing semantic ties. Generating more categories results in greater overall brainstorming performance, because drawing from more categories creates more opportunities to stimulate individual ideas (Nijstad & Stroebe, 2006), which in turn increases the chances of more novel ideas (Stroebe et al., 2010; Stroebe & Diehl, 1994). However, the idea-generation literature has identified communication issues as among the most potent inhibitors of team brainstorming performance mainly because of the potential of communication to interfere with category generation (Diehl & Stroebe, 1987; Nijstad et al., 2002). Having to monitor team speaking patterns to recognize when it is acceptable to speak up to suggest an idea, while simultaneously maintaining an idea in working memory, leads to higher cognitive load (Nijstad & Stroebe, 2006). Higher cognitive load causes more mental image disruption, which reduces cognitive flexibility and leads to fewer idea categories (Nijstad et al., 2010; Stroebe et al., 2010). Therefore, a team’s ability to coordinate and its role in facilitating communication is most likely to affect brainstorming performance via the number of idea categories they generate (Diehl & Stroebe, 1987; Paulus & Yang, 2000; J. Zhou & Hoever, 2014).  

To improve team brainstorming performance, researchers have experimented with structuring the team’s interaction to reduce process loss. One such alteration is a hybrid brainstorming structure, which incorporates individual and collective work portions in many forms. For example, across three experiments, Korde and Paulus (2017) compared four types of brain-writing team structures (wherein team members write down their ideas without talking). They found that alternating collective and solo work was superior to only collective or solo work. Indeed, on average, hybrid structures have been shown to enhance performance relative to collective team structures, as evidenced by their effectiveness in laboratory experiments (Girotra et al., 2010; J. Zhou & Hoever, 2014). Such structures benefit brainstorming teams because process loss is not possible during the individual work portion of the hybrid structure, allowing members to freely generate and record ideas and enabling a team to approach its potential (i.e., summed individual abilities). In addition, process gain is still possible during the collective work portion via cognitive stimulation, allowing a team the opportunity to achieve strong synergy by generating more ideas and by building collaboratively upon the ideas generated by members in isolation (Girotra et al., 2010). The techniques, initially developed in traditional brainstorming teams working together face-to-face in a laboratory or organizational setting, have been extended to a variety of environments, including those that are electronically-mediated and/or asynchronous (De Vreede et al., 2010; Michinov & Primois, 2005). Some meta-analyses demonstrate that distributed or virtual teams using electronic brainstorming tend to generate a larger number of ideas and more creative ideas than traditional face-to-face teams, mainly as group sizes grow to be larger than four members (Dennis & Williams, 2005; DeRosa et al., 2007).  

Finally, in addition to refining practices to enhance team creativity by improving idea generation in teams, other research has focused on the subsequent process of selecting the best among the generated ideas or solutions (e.g., Rietzschel et al., 2006). The quality of the selection processes a team uses in working on a creative task is driven by the same dynamics as other team decision-making processes, described above. However, the emphasis a team places on idea generation versus selection can be driven by factors such as the affective state of team members. For instance, Emich and Vincent (2020) found that teams with more promotion-focused members (emphasizing gains) focus more on idea generation, whereas those with stronger prevention-focused tendencies emphasizing losses emphasize idea selection more. Furthermore, despite the focus by some researchers on idea generation, other research has demonstrated that teams that generate more ideas do not necessarily select better ideas, potentially because of a tendency to focus on ideas generated earlier in the process, which tend to be higher in feasibility and lower in originality (Johnson & D’Lauro, 2018).  

Team Conflict  

Another team process that has been the focus of a growing body of work is team conflict (McGrath & Argote, 2001), which is not a process most teams seek to engage in but is nearly endemic to teams given that the impetus for team formation is typically to bring together individuals who see things differently. The variation in perspectives can be accompanied by fundamental differences in perceived goals (e.g., see Cronin & Weingart, 2007), and this variation is a common basis for conflict. Team conflict, wherein members oppose one another (Weingart et al., 2015), can be productive if it allows for effective integration of those perspectives, error correction, etc. Still, it can also be dysfunctional if it derails a team’s information processing or communication.  

Much of the research on team conflict that has sought to tease out its benefits and harms has focused on how different types of conflict affect team outcomes. For instance, early work on types of conflict identified distinctions between conflict about relationships (e.g., personality clashes), the task (e.g., the goal), and the process (e.g., what to do) (e.g., what to do; Jehn, 1995; Jehn et al., 1999), with more recent research also focusing on disagreements over relative status, or status conflict (Bendersky & Hays, 2012). Meta-analyses on the first three have revealed equivocal results about task conflict’s effect on team outcomes, and generally negative effects associated with relationship and process conflict for team outcomes (De Dreu & Weingart, 2003; de Wit et al., 2012). Mixed results, particularly for task conflict, have led researchers to explore different conflict features that could help to explain the inconsistent conflict-outcome relationships. For example, in contrast to the standard view that conflict is a shared property of the group (and thus appropriately assessed using an average of individual survey ratings of the extent of conflict in the team), Sinha et al. (2016) explored the possibility that there is meaningful variance in conflict perceptions. They found that task conflict is more beneficial when positively skewed, with few team members perceiving high levels of conflict and most team members perceiving low levels of conflict. This occurs because the minority of team members who perceive higher task conflict will introduce divergent viewpoints to the team in a more cooperative and conciliatory manner, leading to more productive, reflective communication. By contrast, when more team members perceive high levels of task conflict, discussions will be more openly contentious and less productive. This is consistent with related research highlighting the dynamic and unfolding process of conflict and its implications for performance, such as where it originates from and evolves to (individual, dyad, subgroup, or team; Shah et al., 2021), and the directness and oppositional intensity of conflict expression, which can lead to conflict spirals versus de-escalation (Weingart et al., 2015). 

Taking a different view, DeChurch et al. (2013) conducted a meta-analysis distinguishing between types of conflict (or conflict states) and conflict management approaches (or conflict processes), finding that conflict processes explained more variance in team outcomes than the type or level of perceived conflict. In particular, they found that teams experiencing conflict achieved better outcomes when managed using more collectivistic processes (such as open discussion and collaboration) versus more individualistic processes (such as avoiding and competing). In addition to exploring the efficacy of conflict management approaches, research has investigated which approach best suits certain types of conflict (e.g., De Dreu & Weingart, 2003; DeChurch & Marks, 2001). In doing so, this work typically assumes that a team’s conflict management approach is independent of the type(s) of conflict experienced, but more recent work offers evidence that conflict types and the intensity of their expression can affect the conflict management approach adopted (Maltarich et al., 2018; Todorova et al., 2022), suggesting a need to heed calls for more work on conflict dynamics (Cronin & Bezrukova, 2019).  

Team Learning  

As mentioned previously in the discussion of team capability, there has been considerable growth in research on team learning over the last three decades, and although some team learning research conceptualizes it in terms of improvement in performance outcomes (Argote & Epple, 1990; Fiol & Lyles, 1985), a growing body of work conceptualizes team learning based on improvement in team process (Roloff et al., 2011). One central area within the team process perspective on learning focuses on information processing in teams in the form of encoding, storing, retrieving, and communicating information (J. M. Wilson et al., 2007) with implications for how team members coordinate knowledge and skill to accomplish tasks (Edmondson et al., 2007; Roloff et al., 2011). Findings in this stream of research stress that teams are best able to perform interdependent taskwork when members know what each other member knows collectively and individually (Roloff et al., 2011), and thus team learning occurs via improvement in this mutual knowledge.  

A separate but related area of team learning research, mainly conducted in field settings, focuses on the factors influencing a team’s climate and its implications for learning (Harvey et al., 2022). One area of research focused on whether a team has a psychologically safe team climate (Edmondson, 1999)—in part based on whether a leader engages in the right behaviors to create it (e.g., Coutifaris & Grant, 2021), which allows team members admit when mistakes have been made (Edmondson, 1996) and to learn from them. Team climate is also influenced by the degree to which team members possess a learning orientation (Bunderson & Sutcliffe, 2003) which shapes how much the team engages in learning behavior such as team reflexivity (Schippers et al., 2003). Related work has also considered the impact of external learning activities, such as seeking outside ideas, help, and feedback (Wong, 2004), as distinct from internal coordination and learning activities (Ancona & Caldwell, 1992; Bresman & Zellmer-Bruhn, 2013; Myers, 2021)—a distinction that has also come to be appreciated more in research on team process more generally, as is discussed next.  

External Activities  

For decades, work on team processes focused almost exclusively on interactions inside the team. However, Ancona and Caldwell’s work starting in the 1980s highlighted the need for teams to interact with entities outside the team—for instance, to share as well as gather information and other resources, and to coordinate with others on deadlines, product design, etc. (Ancona, 1990; Ancona & Caldwell, 1988, 1992). This research identified critical "external activities" (Ancona & Caldwell, 1988). It broadened many team scholars’ attention, fueling interest in multiple lines of inquiry, including work on boundary spanning in teams, wherein teams coordinate with and manage relationships external to the team (Marrone, 2010), and the importance of engaging in and balancing internal and external activities. This balance has been discussed both generally (Ancona & Bresman, 2007; J. N. Choi, 2002; Gibson & Dibble, 2013) and with specific attention to coordination (Ziegert et al., 2022) and learning (Bresman, 2010; Bresman & Zellmer-Bruhn, 2013; Myers, 2021; Wong, 2004). At the same time, complementary work from a network perspective has explored the value of a team having both internal cohesion and range, underscoring the importance of understanding a team’s internal interactions and connections with key external stakeholders (Reagans et al., 2004).  

Although the research on teams’ external activities reflects essential processes that can enhance effectiveness, there are some caveats. First, the importance of external activities hinges on the team’s level of interdependence with external entities; it stands to reason that external coordination is only beneficial when it enables teams to access critical information they could not access otherwise (Woolley et al., 2013). Second, there may be variance in the value of external activities depending on who is doing that work; although the leader may be able to effectively engage in external activities in some teams (Hirst & Mann, 2004; Hogg et al., 2012), other situations—for instance when the work is more dynamic—may call for the involvement of multiple team members to be involved in external coordination (Carson et al., 2007; Guo et al., 2022; Zhao & Anand, 2013). Third, because teams work with limited attentional capacity and resource constraints, they must balance internal and external work (e.g., Myers, 2021; Ziegert et al., 2022). Teams might accomplish this balance by cycling between internal and external coordination over time (Ancona, 1990; Ancona & Caldwell, 1988, 1992; J. N. Choi, 2002), reducing the resources needed to accomplish either one (Myers, 2021) and finding synergies between the two that may allow for transcending some attentional limitations (Mayo, 2022).  

Emergent States  

Whereas research on team processes has generally focused on the behavioral component of team interaction, affective, cognitive, and motivational components are more commonly manifested via emergent states. Emergent states, also referred to by some as "psychosocial traits" (Cohen & Bailey, 1997), are more dynamic and, in contrast with team processes, do not represent team interaction or actions that lead toward specific outcomes (Marks et al., 2001). A phenomenon is characterized as "emergent" when it "originates in the cognition, affect, behaviors or other characteristics of individuals, is amplified by their interactions and manifests as a higher-level, collective phenomenon" (Kozlowski et al., 2013). There are some debates in the literature about what constitutes a collective “trait” versus a state or a process. Thus, some constructs are categorized differently in different areas of the literature. This section discusses a few of the more common constructs about which there is consensus regarding their characterization as cognitive, affective, and motivational emergent states.  

Cognitive States  

Given the emerging view of teams as information processors (Hinsz et al., 1997), most of the literature on emergent states focuses on constructs related to team cognition. Team cognition is how knowledge for team functioning is mentally organized, represented, and distributed within a team (DeChurch & Mesmer-Magnus, 2010). Emergent cognitive states are based on members’ knowledge, beliefs, or perceptions. Several frameworks articulate the distinction between team cognitive processes (such as team learning and information sharing) and team cognitive representations, with the former involving behaviors. At the same time, the latter consists primarily of emergent states (Mohammed et al., 2021). Regarding team cognitive emergent states, two of the most widely researched constructs are team mental models (Mohammed & Dumville, 2001) and transactive memory systems (Ren & Argote, 2011; Wegner, 1987).  

Team mental models (TMM) are "team members’ shared, organized understanding and mental representation of knowledge about key elements of the team’s relevant environment” (Mohammed & Dumville, 2001). TMMs are the most commonly researched form of a convergent form of cognitive representation (Mohammed et al., 2021), where the focus is on the similarity or convergence of knowledge (or mental models) across team members. TMMs include a broader range of cognitive content than other types of team cognition, in that they are based on similarities in team member knowledge about taskwork (i.e., what work needs to be accomplished) as well as teamwork (i.e. how team members will work together to accomplish it Cannon-Bowers et al., 1993; Mohammed et al., 2010). The “accuracy” of a TMM can be evaluated based on the degree of similarity of the mental model across team members, and/or whether or not the mental model is aligned with a “ground truth” or leads to better decisions. 

Another emergent cognitive state that has been the focus of a large and growing body of research is team transactive memory systems (TMSs). A transactive memory system is a collective system that teams use to encode, store, and retrieve knowledge (Ren & Argote, 2011; Wegner, 1987). In contrast to TMMs, TMSs represents a divergent form of collective cognition, in that the emphasis is not on maximizing the overlap of team member knowledge but, instead, maximizing members’ unique knowledge, which can be provided to others as needed, thus increasing the team’s total information capacity (Mohammed et al., 2021). A sizable and growing literature demonstrates the role of TMSs in improving team performance, particularly on tasks where unique expertise is needed (Ren & Argote, 2011). In a meta-analysis of the team cognition literature, DeChurch and Mesmer-Magnus (2010) reported that although team cognition explained significant variance in team performance after accounting for behavioral processes and motivation, the "compilational" forms of team cognition (where different members hold different expertise, such as a TMS) had a more substantial effect than convergent or "compositional" cognition (where there is higher overlap in what members know, such as with a TMM).  

In their review of cognitive emergent states, Rapp et al. (2021) also mention two other widely researched cognitive emergent states—team trust and team climate. Trust is defined as the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action necessary to the trustor, irrespective of the ability to monitor or control that other party (Mayer et al., 1995). One well-known conceptualization distinguishes cognition-based and affect-based trust (McAllister, 1995). Cognition-based trust is generally founded on information about another party’s competence or reliability. In contrast, affect-based trust is driven by perceptions that individuals have genuine care and concern for others, which tends to be reciprocated (Dirks & de Jong, 2022). Whereas a large body of work has demonstrated the benefits of team trust for a variety of outcomes, including team learning and performance, more recent studies have shown the additional importance of trust consensus or the degree to which team members agree about the level of trust in the team and the ways that factors such as team national culture diversity can undermine it (B. de Jong et al., 2021). Similarly, team climates are shared perceptions of the norms and expectations operating in a particular context (Coultas et al., 2014; Rapp et al., 2021; Schneider et al., 2011). A common construct in team climate research is procedural justice, the perceived fairness of decision-making procedures (Cropanzano & Schminke, 2014). As discussed in the earlier section on "Team Composition," characteristics of team members can affect the development of team cognitive states, including team climate, where qualities such as team diversity can reduce the strength of team climate by altering team members’ consensus on the shared norms or expectations that support team climate development or its impact on member behavior (e.g., Colquitt et al., 2002; B. de Jong et al., 2021).  

Emergent cognitive states and collective intelligence  

Emergent cognitive states play an essential role in team collective intelligence, and scholars theorize that collective intelligence emerges as a product of three interdependent collective cognitive systems: transactive memory, transactive attention, and transactive reasoning (Gupta & Woolley, 2021; Woolley & Gupta, 2024). Transactive memory was described earlier in this section, and its close companion, the transactive attention system (TAS), is a dynamic system consisting of members’ attentional capacity, their understanding of other members’ focus and availability, and the inter-member, transactive processes that facilitate allocation of individual attention and retrieval of joint attention when needed. A TAS is theorized to complement a TMS as teams distribute tasks and information, balancing between optimizing member expertise (via TMS) and attentional resources (via the TAS). The TAS involves three inter-member transactive processes that facilitate the efficient utilization of collective attention: (1) updating understanding of member availability, which involves keeping track of one’s own as well as others’ focal priorities and ongoing workload; (2) allocating member attention, via a shared set of norms to signal and update attentional priorities; and (3) attention retrieval when synchronous work is required or an urgent need is uncovered. Teams with strong attentional retrieval mechanisms will exhibit organized patterns of synchronous attention, sometimes manifest as “burstiness,” where periods of independent work are punctuated by periods where members are highly responsive to each other’s requests (Mayo & Woolley, 2021; Riedl & Woolley, 2017).  

In addition, to complement and oversee a TMS and TAS, which work together to ensure efficient and effective utilization of available team member cognitive resources, there must also be a transactive reasoning system (TRS) to maintain collective intelligence. Collective reasoning serves to help teams evaluate collective goals in the context of a constantly changing environment to ensure the pursuit of those with the most significant value, and to facilitate alignment between individual and collective goals (Bacharach, 1999). Building on work on transactive goal dynamics (Fitzsimons et al., 2015; Fitzsimons & Finkel, 2018), a TRS is a dynamic system comprising member’s knowledge of their own and others’ goals and motivational needs and transactive processes whereby members maximize joint rewards via negotiation and alignment around these goals and priorities. A TRS involves three inter-member transactive processes that reduce experienced uncertainty by facilitating individual reasoning: (1) updating occurs through observations of the environment and observations of other members; through experience and interaction with other members, individuals will observe what others are more responsive to or learn what they care about and draw inferences about their goals; (2) allocation of priority, in that once identified, collective goals will need to be negotiated and prioritized to ensure focus on those that are most rewarding to the collective as a whole; (3) retrieval and reinforcement of member commitment, as when members adopt a dense set of goals that are aligned with others’ goals, the collective will be able to devote more resources towards their joint goals and is likely to experience successful outcomes (Gupta & Woolley, 2021). Ultimately, the guidance on goals and priorities from the TRS shapes decisions about resources to enable the TAS and TMS processes to execute effectively. In this manner, a TRS, TAS, and TMS emerge and mutually regulate one another in response to environmental changes and, when doing so effectively, result in the emergence and maintenance of collective intelligence (Woolley & Gupta, 2024). In this manner, the development and maintenance of collective intelligence enables teams to solve problems and accomplish goals that vary in complexity in a wide range of environments (Riedl et al., 2021; Woolley et al., 2010).  

Affective States  

Affective states represent another emergent state with a growing presence in the literature. Examples of affective states include collective emotion (and related constructs) and emergent states that are influenced by emotion but combine with other inputs from the team and task context to affect the team’s behavior, a few of which are described below. Scholars define collective emotion as "macro-level phenomena that emerge from emotional dynamics among individuals responding to the same situation" (Goldenberg et al., 2020, p. 155). An important aspect of collective emotion is the dynamic whereby participants’ emotions influence one another, such as via emotion contagion (Barsade & Knight, 2015; Kelly & Barsade, 2001), leading individuals’ emotions to change as a result of observing others’ emotions, resulting in emergent properties (Goldenberg et al., 2020). This underscores an essential distinction between collective emotion and works on a similarly-named concept—team-based emotion—where the latter results when individuals experience emotion in response to an event that is perceived to have relevance to a group or team with which they strongly identify, such as when one’s political party wins a big election (Goldenberg et al., 2016; Smith & Mackie, 2016). Importantly, team-based emotion can arise even when team members are not aware of what other members are feeling or even who they are; thus, the primary source of their shared emotion is their common identification with a particular team and, consequently, their similar response to the same event (Mackie et al., 2000; Niedenthal & Brauer, 2012). Thus, the critical distinction is that collective emotion emerges as a product of individuals’ emotional responses to an event and one another’s responses. In contrast, team-based emotion emerges as the collective individual response to an event (Niedenthal & Brauer, 2012). In addition, in both collective emotion and team-based emotion, observers from outside the team may not be accurate in their assessment of a team’s emotion, as studies show that there is a tendency for perceivers to focus on faces exhibiting strong or extreme emotion, and consequently to perceive collective emotion to be more extreme than it is—a phenomenon referred to as the "crowd-emotion-amplification effect" (Goldenberg et al., 2021).  

In addition to collective emotion, Rapp and colleagues (2021) identify team cohesion and psychological safety as two other prominently studied emergent affective states. Team cohesion is "the tendency for a team to stick together and remain united in pursuing its instrumental objectives and/or for satisfying members’ affective needs” (Carron et al., 1998, p. 213). There has been some debate about the relationship between team cohesion and team performance (Rapp et al., 2021). However, studies using longitudinal methods do support a reciprocal relationship between team cohesion and performance, whereby the evidence of the cohesion-to-performance relationship is slightly stronger than the reverse direction (Braun et al., 2020; Mathieu et al., 2015), with related work demonstrating a role for moderating factors such as task interdependence (Gully et al., 2012) and time (Mathieu et al., 2015). Researchers have also identified several antecedents or influences of cohesion—mainly factors that reduce or inhibit its development, such as negative interpersonal relationships (J. P. de Jong et al., 2014) and team diversity, particularly diversity leading to faultlines (Molleman, 2005).  

Team psychological safety is defined as a shared belief amongst individuals about whether it is safe to engage in interpersonal risk-taking in the workplace (Edmondson et al., 2007; Edmondson & Lei, 2014; Frazier et al., 2017; Newman et al., 2017). In a psychologically safe work environment, team members feel that others will not reject them for saying what they think, admitting a mistake, or asking for help (Edmondson, 1999), making it safe to experiment and take risks. Consequently, as discussed previously, psychological safety has been widely demonstrated to lead to higher rates of team learning behavior (Edmondson et al., 2007) and, consequently, many other performance benefits in teams (Edmondson & Lei, 2014).  

Motivational States  

Motivational states are a form of team emergent state that reflect team beliefs and influence the intensity, direction, and effort regulation team members exhibit in the service of team task accomplishment (Rapp et al., 2021). Among the most researched motivational emergent states are those related to team confidence, particularly team potency and collective efficacy. Team potency refers to the collective belief in a team that it can be effective (Guzzo et al., 1993). Collective efficacy is the team’s belief that it can successfully perform a specific task (Gibson, 1999; Lindsley et al., 1995). Extant work on these two constructs differentiates them by pointing out that collective efficacy tends to be task-specific, and potency is a shared belief about general effectiveness across multiple tasks encountered by teams in complex environments (Guzzo et al., 1993). Although both constructs contribute to the level of motivation in a team by providing team members confidence that they can achieve a particular outcome, they are different from more general concepts of collective motivation in that they are not based upon the desire to achieve a specific outcome or obtain a particular reward (Guzzo et al., 1993). Overall, the literature on these and related team confidence constructs is robust, demonstrating consistent relationships with performance and mediating role in the impact of variables such as team climate on performance (Gully et al., 2002; Rapp et al., 2021).  

Emergent affective and motivational states and collective intelligence  

Whereas emergent cognitive states play a significant role in collective intelligence, research has repeatedly demonstrated a lack of a relationship between emergent affective states and collective intelligence. For instance, multiple studies have examined and found no support for a relationship between team cohesion or psychological safety and collective intelligence (Engel et al., 2014; Woolley et al., 2010), and studies incorporating physiological measures of affect further demonstrate that although positive affect is associated with team satisfaction, neither positive affect nor team satisfaction were related to collective intelligence (Chikersal et al., 2017; Woolley et al., 2010). These findings are further echoed in related studies of collective socioemotional states, such as liking or perceptions of relationship quality, which are also unrelated to collective intelligence (Woolley & Aggarwal, 2020). At the same time, collective effort, an emergent state stemming from team member motivation, is consistently associated with collective intelligence (Ostrowski et al., 2022; Riedl et al., 2021; Woolley et al., 2022). Future research should examine the interplay of affective and motivational states in the emergence of team collective intelligence, as there may be indirect or moderated effects when the two are considered together. 

Putting It Together: Emergent Team Phenomena  

Although this chapter discusses team processes and emergent states as distinct from each other and team composition, structure, and performance, more and more teams researchers are recognizing their interconnections as part of teams' complex, dynamic nature. Consequently, team composition and structure may set the stage for team processes and emergent states, and those phenomena, in turn, may influence and shape team composition and structure (Arrow et al., 2001; Cronin et al., 2011). Particular team climates, for example, might lead some team members to leave the team and others to join (thus affecting team composition) and lead to different power and status relationships among members, which change team structure. Thus, some researchers theorize that emergent team phenomena include not only emergent states but also behavioral patterns and structures (Waller et al., 2016) that are irreducible to lower-level phenomena due to this recursive relationship (Mackie et al., 2000). Consequently, although this chapter describes team interdependence as a result of the structure of the team and its work routines (Wageman, 1995), the field has also witnessed growing evidence of emergent interdependence (Caruso & Woolley, 2008; Mayo, 2022; Wageman & Gordon, 2005), where the degree to which a team works interdependently is a joint function of team structure and the emergent properties of collaboration.  

NEW TRENDS IN RESEARCH ON TEAMS  

New Team Forms  

Increasing decentralization and globalization of work practices, together with growth in communication technologies, have led to a rapid acceleration in the adoption of virtual work since the 1990s, in which members are geographically dispersed and coordinate their work predominantly with electronic information and communication technologies (Hertel et al., 2005; Raghuram et al., 2019). Entirely virtual, geographically distributed teams have been a common practice for many years (Gilson et al., 2015; Jarvenpaa & Leidner, 1999), but this format became even more widely adopted in response to the restrictions associated with the COVID-19 pandemic in the early 2020s (Dingel & Neiman, 2020; Neeley, 2021). In research at the start of that pandemic, all varieties of “virtual teams” were undifferentiated by researchers, but over time, “virtuality” became conceptualized as a continuous dimension reflecting different levels or degrees (Kirkman & Mathieu, 2005; Wildman et al., 2012). Eventually, teams working and communicating in a shared physical space with little to no technological intervention were considered “low virtuality,” whereas teams conducting work remotely and using technology for all coordination and communication were considered “high virtuality” (Kirkman & Mathieu, 2005). Researchers have also articulated various indices of the level of dispersion in a team, incorporating temporal and geographic separation, which can contribute additional complexity to conducting synchronous work (O’Leary & Cummings, 2007). A further consideration is how distributed teams are configured in terms of subgroups (Carton & Cummings, 2012), where the concentration of members in just two sites can lead to an “us versus them” mentality and worse collaboration than when team members are fully distributed, each working at their location (O’Leary & Mortensen, 2010). In addition to spatial and temporal separation, scholars have also explored the role of the subjective perception of proximity, which, for instance, is associated with more frequent communication and shared identification (O’Leary et al., 2014).  

Advances in technology and the associated rise of virtual or distributed teamwork have also given rise to new ways of organizing teams in many settings, resulting in the evolution of new team forms, which, when considered in light of the traditional definitions of a "team" described in the introduction, begin to stretch the meaning of the word (Mortensen & Haas, 2018; Wageman et al., 2012). Whereas workers in organizations may have been assigned to work primarily with one team in the past, today it is more common than not that individuals are contributing to multiple teams simultaneously (Margolis, 2020; O’Leary et al., 2011) and organizations rely not just on individual teams to produce outcomes, but also on multiteam systems (Mathieu et al., 2001; Zaccaro et al., 2020) or "teams of teams" working interdependently. At the same time, team membership in any given team has become more dynamic (Wageman et al., 2012), leading researchers to relax traditional approaches to defining teams as "bounded" or "stable" and giving rise to research on temporary teams (S.-H. Kim et al., 2022; Valentine & Edmondson, 2015), fluid teams (Bushe & Chu, 2011; Huckman & Staats, 2011; Kerrissey et al., 2021; Valentine et al., 2018), dynamic teams (Mayo, 2022) and flash teams (Retelny et al., 2014), to name a few examples. The resulting new forms of teams have developed in response to increased needs for adaptability (e.g., teams can form and shift in membership as the environment demands; Gupta & Woolley, 2021; Mayo, 2022; O’Leary et al., 2011). However, some have noted that by relaxing the focus on dimensions once viewed as definitional to identifying a "team" (i.e., Hackman, 1987), researchers have shifted the concept such that teams are now less a function of who their members are, and more a vehicle for combining the inputs necessary to accomplish a particular outcome (Humphrey & Aime, 2014), blurring the distinction between teams and groups. As we push this notion even further, there can be "teams" whose members do not all know who else is also part of the team (Wageman et al., 2012), mainly as work becomes increasingly distributed, complex, and dynamic. Alongside these conceptual developments have been methodological developments, such as a resurgence in the use of a network perspective—a perspective used in early laboratory work on teams to study communication patterns (Bavelas, 1950) and now once again essential in many settings where there is a need to identify interdependent "team" activity as well as ways to support new forms of teamwork (Park et al., 2020). This section briefly reviews research on a few of these new team forms.  

Multiple Team Membership  

Multiple team membership (MTM) is a term used to describe situations in which individuals are members of multiple teams simultaneously. This is an arrangement that is increasingly common in many work settings, where some estimate that 65 to 95 percent of knowledge workers across many industries work in some form of an MTM arrangement (O’Leary et al., 2011), on average working on three to four teams although some studies report as many as 20 (Fricke & Shenbar, 2000; O’Leary et al., 2011; Rapp & Mathieu, 2019). The spread of individuals across teams has arisen in step with increases in specialization. It allows organizations to organize employees more efficiently while enabling knowledge transfer across teams. Across levels of analysis, two key features of MTMs—number and variety—have been theorized and demonstrated to have implications for individual and team learning and performance.  

The most studied feature of MTM is the number of teams on which individuals work. At the individual level, working on multiple teams can lead individuals to seek more efficient work processes and thus enable more productivity. However, at a certain point, the number of teams an individual works with can fragment attention enough to limit, or even harm, productivity (Fricke & Shenbar, 2000; O’Leary et al., 2011). The field lacks consensus, though, on the number of teams at which this tipping point arises (Margolis, 2020). This could be partly because the effects of MTM numbers can hinge on time. For instance, although increasing an employee’s number of team assignments initially detracts from individual performance, there can be positive effects in the long run, when employees may be able to capitalize on the benefits of social capital gained through MTM (van de Brake et al., 2018, 2020). It could also be complicated by multilevel effects; for instance, Berger and colleagues (2022) find that the level of MTM in an organization enhances firm productivity, but the number of teams an individual juggles increases their emotional exhaustion, especially when firm MTM is high. In contrast, individual MTM does not impact emotional exhaustion when firm MTM rates are low. Shifting to the team level, scholars have theorized, via team-level mechanisms, a similar curvilinear relationship between MTM number and team performance (O’Leary et al., 2011). At low levels, O’Leary, Mortensen, and Woolley (2011) theorize that the number of teams to which individual members are assigned can benefit team performance by enabling improvements to team norms. Still, after a tipping point, they suggest higher numbers of team assignments can inhibit productivity by introducing lags and delays. Although these initial arguments were theorized and supported with suggestive evidence from the literature, subsequent empirical work has demonstrated this theorized curvilinear effect in RD teams (Bertolotti et al., 2015).  

Another commonly-studied feature is MTM variety, which refers to diversity in "roles, network ties, functional experience, and industry background and team’s tasks, norms, locations, and technologies in use" (O’Leary et al., 2011 :464). At the individual level, greater variety can increase the risk of overload, thereby diminishing productivity (Fodor et al., 2021; van de Brake & Berger, 2022). Research also suggests that MTM variety primarily detracts from team performance because team members who are spread across multiple varied teams are likely to have different representations of their work, lack a transactive memory system, and generally face coordination challenges (Firth et al., 2015; Gupta & Woolley, 2018; O’Leary et al., 2011).  

To the extent that research has shed light on the drivers of successful MTM management, there are factors similar to those that support teams in non-MTM contexts, such as communication and clear goals, but also factors specific to the MTM context such as balancing MTM number and variety (O’Leary et al., 2011) and offering clear prioritization across projects (Fricke & Shenbar, 2000). In most settings, only leaders would be in a position to direct the assignment of workers to teams to achieve these ideal conditions, which is challenging because in many knowledge-work settings where MTM levels are highest, leaders do not have visibility into all of the projects to which different individuals are contributing (Mortensen et al., 2007; O’Leary et al., 2012) leading to potential disparities in how assignments are distributed and burnout and exhaustion for some workers (Berger et al., 2022). 

Multiteam Systems  

A close cousin of MTM, multiteam systems (MTSs) are defined as “two or more teams that interface directly and interdependently in response to environmental contingencies toward the accomplishment of collective goals" (Mathieu et al., 2001). Whereas MTM creates interdependence among teams by sharing team members, MTSs are interdependent with respect to inputs or resources (which can include members and other resources), in addition to the interdependence of processes and/or outcomes (O’Leary et al., 2012). Examples of contexts where MTSs are common include the development of major software systems, management of operations in large organizations, or major projects such as the launch of the Mars Climate Orbiter (Shuffler & Carter, 2018), which all require the input of a variety of teams that often vary in expertise and/or physical location or timing of work schedule. MTSs are increasingly common across various industries, from disaster response to defense, aviation, space exploration, research, and healthcare (Shuffler & Carter, 2018; Zaccaro et al., 2020).  

Inter-team coordination is particularly important to MTS effectiveness (DeChurch & Marks, 2006; Zaccaro et al., 2020), though—just as in any other team—MTS teams must also maintain a balance with intra-team coordination behaviors (Ziegert et al., 2022), or match the balance of their internal and external coordination to the level of interdependence with other teams (i.e., spending more (less) time on inter-team relations when there is more (less) interdependence between teams Marks et al., 2005). Much like non-MTS teams, MTS effectiveness is also impacted by team emergent states, including shared mental models for interaction (Murase et al., 2014) and shared task representations (Firth et al., 2015). A final key driver of MTS effectiveness demonstrated in extant work is leadership, both by the lead team within the MTS and the leaders within component teams (Zaccaro et al., 2020). 

Dynamic Team Membership  

The trends in increasing MTM and MTSs are both products and drivers of the level of dynamism in teaming more generally, where team members need to attend to both coordination with teammates as well as external interactions with other linked teams (Ancona & Bresman, 2007) as teams have a growing dependence on an increasingly broad network of individuals and teams. However, theorizing about or studying the drivers and consequences of MTM or MTS work arrangements usually requires that researchers can identify who is or is not a member of the different interdependent teams, which is becoming more difficult due to the concomitant rise of even more dynamic team forms (Mortensen et al., 2007; Mortensen & Haas, 2018; Wageman et al., 2012). For example, research on "fluid" teams has illustrated the rise in rates of team membership change over time (Kerrissey et al., 2021), the frequent reconstitution of teams (Huckman & Staats, 2011; Valentine et al., 2018), as well as the expansion and retraction of the team boundary over time to incorporate new expertise (Bedwell et al., 2012; Bushe & Chu, 2011), all of which create ambiguity about who is or is not a member of any given team. One common configuration that has evolved in dynamic MTM environments is to identify a small "core" group of members who are more regularly involved in the work of a focal team, and who coordinate with "periphery" members who provide narrower and more specialized contributions to the team’s work, often intermittently (Cummings & Pletcher, 2011; Haas, 2006; Margolis, 2020; Tannenbaum et al., 2012). As these more dynamic forms of teamwork continue to grow, writers such as Mathieu and colleagues (2008) increasingly describe “modern-day” teams using terms such as "temporary"—sometimes involving members who are “complete strangers”—as teams face shifting boundaries resulting from constant reconfiguration to accommodate frequent changes in membership. Consequently, these "dynamic teams" need to develop methods of managing their boundary so that others who are external to the core group but whose contributions are critical to their work can be temporarily integrated into the team as necessary (Mayo, 2022). 

Although research on dynamic teams is relatively nascent, in some of this newer work, researchers seek to find strategies that organizations can use to make these teams effective. On that front, there is some evidence that, as in traditional teams, composing dynamic teams with members who are familiar with each other (or have shared work experience) can lead to increased effectiveness (Akşin et al., 2021; S.-H. Kim et al., 2022). Additionally, using physical space to demarcate clear, if only temporary, boundaries to identify who is involved in the team’s work can enhance coordination and productivity (Valentine & Edmondson, 2015), and the resulting shared spaces can also enable learning (Myers, 2022). When the nature of the team’s work is such that creating physical boundaries is not possible, core members can leverage “scaffolds,” such as team starts, as mechanisms to develop shared structures for coordinating, including defining clear team member roles and routines, to facilitate the development of emergent interdependence with each other and with external members as the team boundary expands and contracts to include external members (Mayo, 2022).  

From Crowds to Remote Collaboration  

An additional new form of team enabled by the advance of new technologies in recent decades is the online "crowd" (Howe, 2008; Kittur et al., 2013). Crowd work, popularized in part by books such as "The Wisdom of Crowds" (Surowiecki, 2004), usually involves a large number of independent contributors who make fairly specific contributions to a well-defined task in a computer-mediated environment with little or no direct interaction with any other participants. Such "crowdsourcing" has grown rapidly over the last few decades and has been used to aid problem-solving in a variety of settings, such as contests to elicit ideas for new products (Afuah & Tucci, 2012; Malone et al., 2017) or software programming competitions held to discover new solutions to challenging problems (e.g., Lakhani et al., 2013). Crowdsourcing has also been helpful for science, both for supplying participants for online research studies (Palan & Schitter, 2018; Peer et al., 2017; Thomas & Clifford, 2017) as well as helping with the coding and analysis of data, such as cataloging data to support biotechnology research (L. M. Chapman et al., 2020; Ellisman et al., 2012) or the development of computer vision systems that require human pattern recognition ability (e.g., Krishna et al., 2016; Von Ahn et al., 2008). Scientific research online has also enabled broader involvement of contributors outside of traditional academic settings, which benefits scientific literacy and education (Aristeidou & Herodotou, 2020; Vaish et al., 2017). Despite some of the essential problems that have been addressed using crowds, many have lamented the limitations of crowdsourcing for tasks that are more interdependent and require more coordination than the collection of inputs from independent contributors can provide (Kittur et al., 2013; Malone et al., 2017).  

Researchers have addressed this gap through new approaches to online teams, in which multi-stage projects (such as developing an app) are broken into parts. The contributions of online collaborators are coordinated via technology as they complete each stage and hand off work to the next stage, enabling more complex projects to be completed by "crowds" (Kittur et al., 2011) or "flash teams" (Retelny et al., 2014; Valentine et al., 2017). Long-term projects involving even broader sets of contributors with specialized expertise have also evolved over time. Early open-source software communities (Lakhani & Wolf, 2003) or contributors to repositories such as Wikipedia have managed collaboration and teamwork via fairly extensive sets of rules, supervised by governing committees that provide management oversight and coordination (Forte et al., 2009; Konieczny, 2010). More recent forms of "collaboratories" draw on decision-making and deliberation tools to facilitate discussion, debate, negotiation, and decision-making to integrate the expertise of different experts on complex problems such as those related to climate change (Gürkan et al., 2010; M. Klein, 2021). As the technology for asynchronous and distributed teams develops further, many researchers anticipate even greater use of various forms of remote collaboration (Brown et al., 2010; Dennis et al., 2008), enabling the design of collaborative teamwork to continue to take on new and more dynamic forms (Gupta & Woolley, 2021).  

Teams and AI  

Many of the new forms of teams discussed thus far have evolved in response to technological developments enabling humans to communicate and coordinate in more extensive and diverse groups across greater distances than was true in the past. Technology plays a pivotal role in all of these new and complex forms, but the collaborators are all human. However, in addition to technology-mediated human teams, there is research on AI-enabled teammates, such as in human-autonomy teams (HAT) in which humans and autonomous AI-based agents function as coordinated units to achieve a common goal (McNeese et al., 2018). In most of the extant HAT research, agents are trained to function independently with specific roles independent of other team members, and teams operate via established routines and processes instead of as adaptive, complex, and self-organizing systems (T. O’Neill et al., 2020). Given the increasingly dynamic nature of teamwork in most settings, some argue that autonomous agents in HATs are not team members unless they are more human-like and interdependent in the activities and outcomes of the other team members (Wynne & Lyons, 2018). Research on whether increasing the use of autonomous agents in teams would be beneficial is mixed. Some studies demonstrate that humans perceive autonomous teammates to be easier to work with. Their collaboration is better when the agents have high levels of autonomy (Azhar & Sklar, 2017), and others demonstrate that a moderate level of agent autonomy is preferred to a very low or high level (M. C. Wright & Kaber, 2005).  

Related studies suggest that the human members’ ability to keep track of an agent may influence the level of agent autonomy perceived as beneficial. For instance, some research demonstrated that participants with low spatial ability experienced the most significant performance benefits from increases in agent autonomy (which meant they did not have to manage and keep track of them), whereas participants with high spatial ability experienced a decrease in situational awareness from increases in agent autonomy (Chen et al., 2013; J. L. Wright et al., 2018). Therefore, it appears that HATs benefit from autonomous teammates who are aware of and can adjust to variations in human team members’ abilities. Despite progress in this domain, several studies illustrate that human-human teams outperform HATs due to more efficient information sharing among human teammates compared to HATs (Demir et al., 2019; McNeese et al., 2018) as well as better organization and adaptation (Grimm et al., 2018). Ongoing research on the implications of communication frequency and quality for team outcomes has become an essential focus of HATs research (Endsley, 2017; McNeese et al., 2018; T. O’Neill et al., 2020).  

New Approaches to Studying Teamwork  

New forms of teamwork have pushed researchers to develop new methods and theoretical perspectives for understanding how teamwork takes place in these new forms. As discussed previously, classic approaches to studying teamwork have traditionally adopted an input-process-output perspective. This eventually evolved into more of a cyclical input-mediator-output-input framework, where “mediator” replaces “process” in the traditional framework but includes a feedback loop affecting the inputs (Ilgen et al., 2005). Still, other calls have grown louder for abandoning this somewhat linear perspective (even with a feedback loop) and more fully embracing the view of teams as complex, adaptive systems (Arrow et al., 2001; Cronin et al., 2011; Mathieu et al., 2019) where multiple influences operate simultaneously and interactively over time, leading to non-linear effects and changes to the system which in turn change the ways it responds to inputs. A related trend in the organizational psychology literature advocates for a multi-level perspective in studying social phenomena (Kozlowski & Klein, 2000; Rousseau, 2011), which requires thinking not only about the dynamics of a particular team but also the influences and constraints coming from the characteristics of the individual team members (which some refer to as the "bottom-up" influences) as well as from the context the team is working within (also referred to as "top-down" influences). The benefit of adopting these more complex approaches to theorizing and studying teams is that researchers may draw incorrect conclusions or miss important influences on teamwork if they do not. Sometimes, researchers observe different teams that seem, on the surface, to have essentially the same inputs and yet, surprisingly, exhibit very different processes or outcomes (Hackman, 2012; Kozlowski et al., 2013). However, once studies incorporate variables from the individual level, such as team member characteristics (e.g., age, gender, organizational tenure) together with higher-level contextual variables (e.g., How competitive is the team’s environment? What other teams do they need to coordinate with?), the reasons for different responses from teams that seem very similar become more apparent. Thus, adopting more complex, multi-level approaches to theory and research and considering external influences and changes over time, can prevent researchers from drawing incorrect conclusions or applying the findings of one study to solve a problem in a different context where a solution may create new problems.  

The new theories and methods developed to enable researchers to incorporate multi-level, complex adaptive systems perspectives into their study designs have also driven the need for new forms and larger volumes of data. Industrial and organizational psychologists have increasingly relied on survey-based self-report data, which some have lamented as displacing behavioral data in most studies (Moreland et al., 2010). However, particularly in field settings, survey measures are often necessary as directly observing teams as they work in real-time is typically impossible. That said, as researchers evolve their study designs to model the complex relationships among many different inputs and their changes over time, they require many measurements of many different variables, most of which are unrealistic or impossible for human participants to provide directly via self-report. Therefore, as teamwork is increasingly carried out in digital environments, researchers have harvested the many different digital traces team members create as they communicate and collaborate to develop new measures of team process, emergent states, and outcomes that can be captured unobtrusively to model how teams develop and change dynamically over time (Mathieu et al., 2022; Riedl et al., 2021). In many ways, such measures are a return to the types of behavioral measures that researchers at one time created through the careful laborious hand coding of observations, which had been displaced by surveys in many studies but that can now be captured much more efficiently using computational social scientific methods (Edelmann et al., 2020; Lazer et al., 2009). In addition, some researchers use computational modeling methods to simulate the dynamics in these complex systems and identify the underlying "rules," articulated in formal mathematical models, that underlie the evolution of the patterns observed (Coen & Schnackenberg, 2012; Shrestha et al., 2020; F. Wang et al., 2007). The development and change in the methodological approaches to teams research has both fueled and been fueled by the growing interdisciplinarity of the field (Hackman & Katz, 2010; Mathieu et al., 2017). Consequently, as we look at trends in research funding and publication outlets for research on teamwork, we see a continued movement away from its original home in sociology and social psychology and into applied areas such as business, communications, and medicine, along with different sub-disciplines of engineering and computer science, a trend commented upon in the chapters reviewing groups and teams research in prior editions of this Handbook (Hackman & Katz, 2010; Levine & Moreland, 1989) which continues.  

The Future of Research on Teams: Calling All Social Psychologists!  

It is an exciting time to be a teams researcher, given all the changes taking place in how teams operate, reflecting changes in society more generally, and the methodological advances in how teams are studied. We hope that more social psychologists will also get excited and see the opportunities they have to contribute to research that has important implications for the lives of many people (Ledford, 2015) as well as to collaborate with scientists in other disciplines to potentially produce high impact research (Uzzi et al., 2013). The growth of research on teams has been driven by the practical, real-world concerns of organizations and even society as a whole, which increasingly relies on the products of teamwork to address pressing needs and solve significant problems (National Research Council, 2015; Wuchty et al., 2007a). These practical needs have led the field of teams research to become more interdisciplinary (Falk-Krzesinski et al., 2011; Fortunato et al., 2018; Lungeanu et al., 2014), which brings with it more opportunities as well as some challenges. Social psychologists must remain engaged in this vital work even as it becomes heavily embedded in other disciplines and applications. Technological advances that enable new forms of teamwork or facilitate the collection of more and different kinds of data can dramatically advance the science of teamwork. Thus, we hope that alongside the new generations of teams researchers cropping up in many applied disciplines, there will also continue to be some who are well-trained and firmly grounded in the intellectual heritage of social psychology so that new developments in teams research can be rooted in and, ideally, contribute to its essential foundations in this field. 

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ENDNOTES

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