Embracing Complexity While Communicating Practicality

When I was preparing for comprehensive exams in graduate school, I remember everyone repeatedly telling me to answer the question being asked rather than the question I wanted to answer. Here, I was asked to write about what I think the next ten years of teams research will look like and instead I’m going to answer what I hope the next ten years will look like.

I hope the next decade of teams research is one that embraces the complexity inherent to teams while clearly communicating practicality. Although this may sound like a contradiction, that view reflects a false dichotomy. Teams are complex, and our research needs to increasingly recognize and model that complexity. The low-hanging fruit has long since been picked; studies that simply regress team performance onto aggregates of everyone’s favorite individual differences are no longer going to cut it. Practitioners already recognize this complexity. When we work closely with them, they consistently emphasize that teams encounter unique challenges and are capable of accomplishing unique things. As scientist-practitioners, our task is to embrace and leverage this complexity to better understand what is happening within and between teams and then translate those insights into practical solutions for real problems.

Where, then, does the complexity lie? Although there are many sources, one of the most fruitful is dynamics. The term “dynamics” has been used broadly to capture several interrelated phenomena. First, dynamics can represent time, or more accurately things changing over time. As a field we have developed robust temporal frameworks including episodic (Marks et al., 2001), event-based (Morgeson et al., 2015), longitudinal (e.g., Ployhart & Vandenberg, 2010), and discontinuous (e.g., Bliese & Lang, 2016). Over the next decade I hope we continue to test and extend these frameworks rather than merely invoking them. Advances in unobtrusive data collection, combined with analytic approaches that span accessible word-count methods (Mathieu et al., 2022) and increasingly sophisticated natural language processing techniques (Pandey & Pandey, 2019), now allow us to address temporal questions that we have collectively discussed for decades (e.g., McGrath, 1991).

A second form of complexity involves inter-relational dynamics. We have made robust meaningful theoretical advances in understanding how relationships evolve and interact over time. Much of this work bridges gaps between temporal and inter-relational dynamics. Teams scholars have done an excellent job theorizing how inter-relationships affect episodic team processes (Crawford & LePine, 2013) and how multiplex relationships influence taskflow under nuanced conditions of conflict (Park et al., 2020). I hope that as our methods have now caught up to our theorizing, we will continue to test and advance these areas, as for instance, there are ripe opportunities to empirically model multiplex relationships among taskflow and different Marks et al. (2001) team processes to address the dynamic inter-relational questions raised by Crawford and LePine (2013).

Third, dynamics also characterize team inputs. Although we frequently acknowledge that team composition changes over time, we too often relegate these dynamics to discussion sections rather than modeling them directly (Wolfson et al., 2022). We can build off the knowledge about the effects of change and instability shaping team functioning and performance (e.g., Rink et al., 2013; Summers et al., 2012), and start to really unpack the underlying dynamics related to change and churn (e.g., Stuart, 2017) and perhaps begin to acknowledge that dynamic team composition is reflected in more than just membership change (Wolfson et al., 2022). I hope future research more fully incorporates dynamic inputs, not just dynamic mediators and outcomes, and begins to rigorously test these ideas.

Of course, embracing dynamics necessarily increases complexity, which invites a familiar concern: how do we ensure practicality? Foundational work in our field has long emphasized that theory earns its value through application, not abstraction alone. Accordingly, we communicate practicality by rolling up our sleeves and working directly in the field, partnering with organizations and grant agencies, and solving problems that matter in those settings. Once we identify what is driving critical variance using our sophisticated models, we must also demonstrate that we can meaningfully intervene. Intervention work and field experiments will force us to engage with the realities of practice, clarify what constitutes a meaningful effect, and communicate actionable guidance. Even a one percent improvement can matter, but its importance depends heavily on context, organizational priorities, and our ability as researchers to communicate its practicality. By way of example, Wolfson and Mathieu (2018) showed that a relatively negligible looking team performance bonus effect—where team-level human capital resources amplified the performance implications of individual-level alignment with situational demands—corresponded, in the centennial Tour de France, to the difference between finishing in first and 15th place.

Finally, I want to briefly note what I hope the field does not look like over the next decade. At times, teams research, like other areas, risks letting the tail wag the dog, chasing new constructs or technologies without sufficiently integrating prior knowledge. World events and technological advances understandably shape our research agendas; COVID-related work and the rise of wearables are clear examples. But relevance should not come at the expense of theoretical continuity. The same caution applies to AI. AI will undoubtedly remain an important area of study, but I hope we do not lose teams research in the process of studying AI. Our rich literature on human-machine interaction (e.g., McGrath, 1984) provides a strong foundation for examining AI as a teammate, particularly with respect to inter-relational dynamics. As AI takes on routine tasks, important questions will continue to emerge: how does this reshape interaction patterns? What opportunities for learning or water-cooler conversations might be lost? And what new expectations are placed on human team members now that the easy tasks are all taken care of by AI?

Overall, I am not just optimistic, but excited about the next decade of teams research. Our theories, data, and methods have reached a point of alignment that creates opportunities for genuinely impactful work. The next decade holds considerable promise if we continue embracing complexity while communicating practicality.