Multiscale Coordination Dynamics: Reflections and Opportunities for Advancing Groups and Teams Research

Abstract

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.

Indroduction

As small groups and teams research enters a period of rapid methodological and technological change, long-standing theoretical questions are resurfacing in new and consequential ways. This resurgence is driven in part by advances in wearable sensing, computer vision, artificial intelligence, and computational modeling that now allow researchers to observe the dynamics of groups and team interactions as they unfold across multiple levels of analysis, making it increasingly clear that teams are not merely collections of individuals, but complex, multilevel systems (Arrow et al., 2000). These advances align with what coordination dynamics researchers have long argued: that individuals and groups are complex systems whose effectiveness depends on how physiology, actions, thoughts, and emotions become organized and change over time (Kelso, 1995, 2009, 2021). Having spent the past decade studying coordination dynamics in dyads, groups, and teams, I have seen the field move shift from coarse behavioral indicators toward rich, multimodal data streams, often outpacing the development of theories needed to explain them, and bringing numerous conceptual and methodological challenges (Wiltshire et al., 2020).

At its core, the study of team coordination asks how collections of individuals achieve outcomes that no individual alone could produce (Gorman, 2014; Gorman et al., 2010). This question arises because even a single individual is already a high-dimensional system in which performing the simplest activities (e.g., speaking a sentence) requires coordination across many components and time scales (Behroozmand et al., 2015; Turvey, 1990; Wiltshire et al., 2017). Teams only multiply this complexity. While traditional team coordination is defined as “orchestrating the sequence and timing of interdependent actions” (Marks et al., 2001, p. 363), coordination, from a complex systems perspective, is defined as the ways system components covary over time, space, and scale (Butner et al., 2014; Kelso, 2009; Turvey, 1990; Wiltshire et al., 2019). By recognizing that team coordination is a multiscale phenomena and a general property of complex systems (van Eijndhoven et al., 2024), coordination is not simply another team process variable, but a foundational organizing principle that sustains adaptive behavior across all types of complex systems (Amazeen, 2018). The urgency for the next decade, in my view, lies in integrating this principle more deeply into how we theorize, measure, and intervene in teams (for related ideas drawn from complexity science see Semin, 2026; Wolfson, 2026).

Opportunity 1: Theory—From Single-Scale to Multi-Scale Functions of Coordination

A central opportunity for the field is to move from largely single-scale theories of coordination toward explicitly multiscale, functionally specified accounts. Coordination reflects the organized coupling among system components and typically serves a functional purpose such as maintaining physiological regulation, enabling joint action, signaling affiliation, or supporting collective cognition (daSilva & Wood, 2024; Nowak et al., 2017; Rico et al., 2008). Importantly, however, the presence of coordination does not guarantee that it is adaptive for a given task, timescale, or context.

Despite this, team coordination research often infers function by examining associations between coordination measures in a specific modality (e.g., physiology, movement, vocal behavior) and team outcomes (Halgas et al., 2022; Kazi et al., 2021; Palumbo et al., 2017). This approach has yielded mixed findings with growing evidence that coordination can support or undermine performance depending on how, when, and where it emerges. For example, movement synchronization has been shown to facilitate collaborative problem solving and team performance (Wiltshire et al., 2019), whereas excessive physiological coordination can predict poorer outcomes under certain conditions (Gordon et al., 2020; Timmons et al., 2015).

Without a multiscale theoretical framework, a critical theoretical gap remains in understanding how the functional role of coordination varies across modalities (e.g., heart rate, skin conductance, movement, vocalizations), time-scales (minutes, hours, days, weeks), and contexts. Bridging this gap requires a theory that explains not only the presence of coordination, but also its adaptive organization across scales. Such a theory should integrate relevant theories across domains. Complex systems theory offers a promising integrative lens by describing how coordination at lower levels (e.g., neural or physiological) can scaffold higher-level behavioral and cognitive organization, while social and task constraints simultaneously shape lower-level dynamics (Nowak et al., 2017). Initial theory building efforts emphasize coordination variety (Gorman & Wiltshire, 2024) and flexible multimodal synchronization (Gordon et al., 2024), yet these and other relevant theories (Geller & Porges, 2014; Hamilton, 2021; Hoehl et al., 2020; Koole & Tschacher, 2016; Wells et al., 2022) remain insufficiently integrated within group and teams research. The next decade calls for theory that explicitly maps coordination across modalities and scales, specifies function, and generates testable predictions about when coordination supports or hinders effective teamwork.

Opportunity 2: Methods—Matching Metrics to Meaning

Methodological innovation in coordination dynamics research has outpaced theoretical and conceptual alignment. An abundance of methods now exist for estimating pairwise and multivariate coordination from cross-correlation and recurrence analyses to information-theoretic and network-based approaches (Cliff et al., 2023; Hudson et al., 2023). While this diversity is a strength, it also creates a major practical challenge: different methods operationalize coordination based on fundamentally different assumptions about what coordination even is.

Some metrics quantify statistical covariation, recurring states, directional influence, or information flow, and these measures do not all map to the same coordination construct (Schoenherr et al., 2019). As a result, findings with one method can reflect qualitatively different underlying dynamics than those that actually exist, and may also fail to replicate across methods. Even when theory motivates clear predictions, researchers often lack guidance on which methods best align with their conceptualization of coordination (Halgas et al., 2022; Hudson et al., 2023).

Progress therefore requires tighter coupling between theory and measurement. The theoretical progress called for above should constrain methodological choices. Complementarily, though, systematic efforts to compare and organize coordination metrics, such as empirically mapping their behavior across tasks, timescales, and noise conditions, can clarify which methods are interchangeable and which capture distinct phenomena (Altmann et al., 2022; Hudson et al., 2023; Klein, 2023). Together, these efforts would improve interpretability, comparability, and cumulative progress by ensuring that coordination metrics are selected not for convenience, but for conceptual fit.

Opportunity 3: Time—Team Coordination Trajectories, Not Snapshots

A third major opportunity is to more fully embrace the temporal nature of team coordination dynamics. Teams exhibit patterns that evolve across minutes, days, and longer project cycles (Klonek et al., 2025). Coordination may stabilize, fluctuate, or undergo abrupt transitions as teams adapt to changing demands. Yet, most studies examine coordination within brief tasks or single interactions, providing limited insight into how coordination develops, persists, or breaks down over time (Kelso, 2009; van Eijndhoven et al., 2023a, 2023b; Wiltshire et al., 2018; Wiltshire et al., 2019).

Longitudinal research suggests that coordination can change directionally and that what is functional at one stage of a team’s lifecycle may become maladaptive at another (cf. Mayo & Gordon, 2020). Concepts from complex systems theory, such as flexibility, phase transitions, and resilience, offer powerful tools for understanding these trajectories (Grimm et al., 2023; Rigal et al., 2020; Scheffer et al., 2012), but more longitudinal empirical work on multiscale team coordination dynamics is needed. Advances in wearable sensing (Kleckner et al., 2021), ambulatory assessment (e.g., Reichert et al., 2020), and digital trace data (David et al., 2024; Newton et al., 2024) now make it feasible to study coordination longitudinally in real-world teams. Leveraging these tools to study coordination trajectories will be critical for understanding team adaptation, learning, and sustained performance.

Implications for the Next Decade

Taken together, these opportunities position coordination dynamics as a central organizing framework for the next decade of small groups and teams research. Realizing this potential will require tighter integration of multiscale theory, conceptually grounded methodologies, and longitudinal empirical designs. Progress will depend on interdisciplinary collaboration across psychology, organizational and computational sciences, and also scientific training models that equip scholars to work across theory, methods, and multiple levels of analysis (Fiore et al., 2019).

Practically, a functional and temporally informed understanding of coordination dynamics holds promise for improving team design, training, and intervention in high-stakes domains such as healthcare, emergency response, and technology-mediated work. By identifying coordination signatures and their trajectories, researchers can move beyond post hoc understandings of team work toward anticipating breakdowns and supporting adaptive teamwork in real-time (Gorman et al., 2019; Li et al., 2025; van Eijndhoven et al., 2023a; Wiltshire et al., 2024). As methodological capabilities continue to expand, the central challenge for the field is not simply to measure coordination more precisely, but to ensure that technological innovation is guided by theory, function, and a multiscale understanding of how teams adapt over time.