This meta-analysis examined how transactive memory systems (TMS) operationalization, task characteristics, and outcome measurements moderate TMS-team outcome relationships in laboratory studies. Based on 44 studies (103 effect sizes), TMS demonstrated a moderate positive relationship with team outcomes ( r = .44, 95% CI [0.36, 0.50], p < .001). Team outcome measurement approach emerged as a significant moderator: self-report scales yielded larger effects ( r = .77) than observer ratings ( r = .38) or embedded metrics ( r = .39). Findings highlight the importance of measurement decisions in TMS research.

Medical teams operating under stressful conditions are prone to experiencing disconnects, which are instances of task-specific incompatibility (i.e., ineffective coordination) between team members. In simulation-based medical team training, we examined (1) whether we can identify disconnects, by locating transitions in low-level team coordination based on data collected with wearable devices, and (2) how medical teams address disconnects based on video-based annotations of different types of high-level team coordination behavior. We find that with our exploratory approach we can identify disconnects with low-level transitions. Moreover, teams resolve disconnects with explicit action and information coordination, but lose valuable time in doing so.

As small groups and teams research undergoes rapid methodological and technological transformation, longstanding theoretical questions about coordination are resurfacing in consequential ways. Advances in wearable sensing, artificial intelligence, and computational modeling now enable fine-grained observation of multilevel interaction dynamics, revealing teams as complex, multiscale systems. Drawing on a decade of research in coordination dynamics, this paper argues for reframing coordination as a foundational organizing principle rather than a discrete team process. We outline three key opportunities for the next decade: developing multiscale, functionally grounded theories of coordination; aligning methodological choices with conceptual meaning through systematic comparison of synchrony metrics; and adopting longitudinal designs to capture coordination trajectories across time. Integrating these directions promises to enhance theoretical coherence, methodological rigor, and practical relevance. Ultimately, a multiscale, temporally informed understanding of coordination dynamics can advance cumulative science and inform interventions that promote adaptive teamwork in high-stakes organizational contexts.

This essay discusses the future of research on group status using a multilevel perspective. Although status is inherently multilevel, researchers in this domain have not fully leveraged this perspective in their empirical studies. We identify three ways group status can, and should, be represented as a multilevel phenomenon: when status ratings are obtained from multiple observers, when individual member status ratings are used to compose group status perceptions or hierarchies, and when status changes dynamically over time. We hope this essay encourages scholars to more fully embrace a multilevel approach, which is essential for advancing research on status in small groups and beyond.

Teams research has never been for those who prefer easy answers. The future of the field will belong to those willing to ask harder questions. We must reduce our reliance on comfortable simplifications and treat variability as substantive, context as fundamental, and artificial intelligence as an active participant in collaboration. The future of teams research will depend on scholars willing to embrace messiness without sacrificing rigor.

Groups and teams research is at an inflection point. Longstanding assumptions of effectiveness are increasingly strained by the reality that teams are characterized by fluid boundaries, unique members, and dynamic interactions, as well as new technologies and heightened societal stakes. This issue integrates a set of reflections by mid-career scholars to identify common challenges and emerging directions for the next decade of teams research. Across their contributions, multilevel complexity emerged as a unifying theme, which can be considered as structural (e.g., diversity, AI, hierarchies, boundary permeability), temporal (e.g., trajectories, key events, relationships), and epistemic (e.g., measuring multiple time points, experiences, modalities). Overall, the authors are clear that understanding teams as dynamic, multilevel, open systems is no longer optional. Importantly, they also offer a clear path forward. We must collaborate outside our core disciplines, embrace new methods, and reconsider several publication norms. In short, we must use teams to understand teams.

In this essay, I highlight the liminality of teams research – where new discoveries can be made by embracing things “in-between.” One liminal space is that between individuals and groups – the patterns of intra- and interpersonal relationships that represent the microdynamics of teams. Another liminal space is that between past and future research philosophies, questions, and methods that will help us discover those patterns. Liminal spaces are transitional and challenging, but they are the very threshold of exciting change.

Artificial intelligence is no longer merely supporting teamwork from the margins. By entering meetings directly as a participant, AI may be changing the way teams function. This commentary argues that note-taking bots, real-time summarizers, and emerging facilitative agents are altering core meeting dynamics. This may include meeting attendance, participation, memory, accountability, and authority. In doing so, AI challenges long-standing assumptions about team boundaries and raises new questions about trust, leadership, entitativity, and human dependence on machine-generated records. Meetings are therefore not just another site of technological adoption, but a critical arena for understanding human–agent collaboration as it unfolds in real time. Rather than treating AI as a neutral administrative tool, this commentary positions AI in meetings as a consequential shift for both meeting science and broader teams’ research.

In this essay, I argue for expanding our definitions of team effectiveness. I highlight how the same dynamics that support coordination and team performance can also enable collective harm, and I suggest that more fully capturing the moral consequences of teamwork will help us maximize the potential of teams in an increasingly complex, technology-mediated world.

This article is a satirical letter to Team-would, the great-nephew of C.S. Lewis’ infamous devil Screwtape. Whereas Screwtape was tasked with corrupting a specific young man, Team-would is tasked with corrupting academics – especially teams researchers. The article suggests that this task can be accomplished in three easy steps: 1) entrap; 2) distract; and 3) obfuscate. Like other satirical pieces, this article is crafted to serve as constructive social criticism that challenges existing norms (e.g., selection and socialization) and structures (e.g., reward and task structures). It also strives to make uncomfortable topics easier to discuss (e.g., by introducing the Sufficiency Serpent and Generalizability Gremlin). Ultimately, we can choose whether to hike uphill and advance team science in service to society, or to follow the slippery slope toward an incoherent system that exists only to sustain itself.