Team Boundary Blurring: Implications for the Next Decade of Teams Research

Abstract

Team boundary blurring has become a defining feature of contemporary teamwork. Building on prior research, this essay conceptualizes boundary blurring along three trends: team fluidity (membership change), team overlap (multiple team membership), and geographic dispersion (through remote/distributed work). These trends are likely to intensify as employment becomes more flexible, project-based work expands, and hybrid work makes virtual collaboration common to a large portion of modern teams. This is critical because boundary blurring implies that team members differentially engage in and observe team interactions, increasing the likelihood that experiences and perceptions of team processes diverge rather than converge. This challenges the widespread reliance on compositional emergence assumptions and consensus-based aggregation in team research. The essay argues for stronger adoption of configural and process-oriented approaches to capture how team interaction structures emerge and evolve over time.

The discussion around clearly identifying what constitutes a team is not new. Over the past 10–15 years, several influential debates have questioned whether the defining boundaries that traditionally characterized “real teams” in Hackman’s sense still hold in contemporary work settings (e.g., Hackman & Katz, 2010; Wageman et al., 2012). Mortensen and Haas (2018), for example, discuss boundary blurring as a core characteristic of modern teamwork. Specifically, they propose three key trends that contribute to team boundary blurring: team fluidity (changes in team membership over time), team overlap (individuals’ concurrent involvement in several teams), and geographic dispersion (team members working from different locations).

There is good reason to expect that team boundary blurring will become even more prevalent in the future. First, employment relationships are becoming less stable, with temporary project-based assignments and freelance work increasingly complementing traditional employment (e.g., Spreitzer et al., 2017; Wu & Huang, 2024). Second, project-based work has expanded across many sectors, and multiple job holding has risen in several labor markets, meaning that more individuals are concurrently embedded in multiple teams—sometimes within the same organization, sometimes across organizational boundaries (e.g., Chen et al., 2019; Rishani et al., 2024). Third, geographic dispersion is no longer limited to prototypical “virtual teams.” Spurred by the COVID-19 pandemic, roughly one quarter of employees across the globe now have access to hybrid work arrangements (Aksoy et al., 2023), in which they can alternate between remote and co-located work. As a result, even teams that formally share a single office site regularly experience periods in which some or all members are physically distributed across different locations (Handke et al., 2024, 2025).

This ongoing change to team boundaries has important implications for us as teams researchers in how we study teamwork, both theoretically and methodologically. Boundary blurring conditions like fluidity, overlap, and dispersion mean that members will differently engage in, contribute to, and experience team interactions. For instance, members who are part of many teams or who often work remotely are likely to be less involved in team interactions than those who are members of only one team and spend most of their time co-located with other members . Moreover, members of the same team may also perceive interactions differently depending on their opportunities to observe them in a shared physical environment, how long they have worked on the team, or on how the interactions compare to those on other teams they work on (see e.g., Handke et al., 2025; O’Leary et al., 2011). This raises a fundamental question: What does it mean for “team” processes and emergent states if members have different experiences of the interactions that give rise to these processes and states?

While seminal work by, for instance, Kozlowski and Chao (2018) and Kozlowski and Klein (2000) discusses the existence of both compositional and compilational forms of emergence in multilevel systems like teams, the dominant assumption in team research is that over time, members will converge in their perceptions and behaviors. Specifically, as Kozlowski (2025) noted in an earlier issue of Small Group Research: “[. . .] when emergence is considered, it is almost always conceptualized as a composition phenomenon, and is treated as a theoretical assumption that is verified by assessing restricted within team variance on the construct in question” (p. 509). In practice, this means we as teams researchers generally assume that we can effectively employ direct or referent-shift consensus items, obtain answers that (hopefully!) present acceptable values on within-team agreement indices, such that we can aggregate individual members’ answer to the team level, and rightfully declare this as “team [construct XY]”. However, how likely is it that team members will hold similar perceptions of, for example, team conflict or team coordination at a given point in time when they differentially engage in or observe interactions with different subsets of team members?

This essay does not intend to discredit compositional models and measurement approaches altogether. However, I believe that we need to consider for which types of teamwork, in which contexts, and in which time frames they (still) make sense. At the same time, we should be more invested in configural approaches that explicitly model interaction structures, as well as methodological designs that allow us to observe compilational emergence as it unfolds over time. Sociometric approaches that capture who interacts with whom provide one important avenue (e.g., Crawford & LePine, 2013; Park et al., 2020), though they can be demanding to implement in large and fluid multi team membership systems, particularly in survey research (see Mathieu et al., 2017). Increasingly, digital trace data (gained through e.g., organizational communication platforms, smartphone-based logging, or proximity sensors)—offer nonintrusive ways to capture interaction patterns “in the wild” and at high temporal resolution (Klonek et al., 2019) without needing to impose pre-defined team affiliations. Analytically, approaches such as relational event modeling can help us understand how the structure of team interactions unfolds over time (Leenders et al., 2016; Schecter et al., 2018).

Taken together, these developments point to a broader opportunity for the next decade of teams research: to rethink what it means to study teams in contexts where boundaries have become increasingly blurred and dynamic. Doing so requires different ways of capturing and analyzing team interactions but also a shift in our theoretical assumptions about emergence, convergence, and the very unit of analysis we call a team.