Why Do Some Teams Perform Better Than Others?

Many academics, practitioners, and system designers use frameworks and models to guide and structure their work. For example, a framework or model might propose an ideal sequence or workflow for working with individuals or groups in a therapeutic, training, or design context or when working to address a societal problem. But do these idealized sequences and workflows derived from abstract frameworks and models make sense in the real world: for example, when a team is working together to adapt to a problem situation? This is the focus of an interesting study by Georganta and colleagues (2021).

Georganta and colleagues remind us that organizations increasingly rely on teams to address complex problems. This includes the ability to respond to ongoing changes, uncertainties, and threats in their environment. The ongoing changes, uncertainties, and threats resulting from the COVID-19 pandemic highlight the need for teams across all domains of the public and private sector to be continuously adaptive.

Team adaptation is commonly seen as important (Maynard, Kennedy, & Sommer, 2015), but much of the available literature is theoretical, pointing to frameworks and models that are assumed to characterize optimal team dynamics. Empirical work focused on the actual dynamics of team adaptation is often absent. Without an empirical understanding of team adaptation dynamics, a team leader or facilitator might assume that a standard team adaptation process framework (e.g., Rosen et al., 2011) can be used to characterize optimal team dynamics.

Notably, Rosen et al. (2011) proposed that a team will adapt successfully to unexpected events by moving through four consecutive activity phases: situation assessment -> plan formulation -> plan execution -> team learning. If a team moves through this activity cycle repeatedly in an effort to adapt to changes in their environment, they will perform well. But do high-performing teams consistently follow this sequence, or are the dynamics of movement between these four phases of team activity different in practice?

How different teams adapt to unexpected events

Georganta and colleagues examined this question by analyzing the activity of 70 teams, each with four members. Team members worked together on a Space Alert task, where they had to coordinate with each other under time pressure and in the face of external threats to protect a spaceship in a game scenario. The analysis of team adaptation activity focused on one game round in particular, which included seven 1-min stages during which team members could attack, move, navigate, or load energy to protect their spaceship. Team performance was measured by reference to the number of 1-min stages a team completed, successfully navigating threats.

Teamwork was video-recorded, and team talk and activity were subsequently coded using a very fine-grained coding method. This included the coding of parts or entire sentences of team talk as a fundamental unit of analysis. In this way, discrete activity phases were identified. Notably, across all teams included in the study, 465 phases of activity focused on situation assessment were identified, 691 phases of activity focused on plan formulation were identified, and there were a further 408 phases for plan execution and 170 phases for team learning.

A statistical method called lag sequential analysis was then used to examine the different sequences of team adaptation phases that occurred. The key question here was: Did the teams run through the activity phases in a way predicted by theory (i.e., sequentially moving through situation assessment -> plan formulation -> plan execution -> team learning), or were other sequences more common? And what does successful team performance look like?

The results of the study revealed that teams adopted a huge variety of activity sequences. Sequences (and sub-sequences) in line with theory were commonly observed (e.g., situation assessment -> plan formulation occurred 75.69 percent of the time), but team dynamics were increasingly complex and variable when the researchers looked at longer phase sequences. For example, the following sequence—situation assessment → plan formulation → plan execution—occurred only 36.08 percent of the time, whereas the "forward-backward" sequences of plan formulation → plan execution → plan formulation, and plan formulation → situation assessment → plan formulation, occurred 57.62 percent of the time.

Notably, the activity of team learning was commonly followed not only by situation assessment but also by plan formulation and plan execution, indicating that teams not only learn toward the end of the team adaptation process but also at different points throughout the process. This makes sense: It is reasonable for teams to evaluate and reflect on their past actions multiple times, in a way that can guide the next steps, which might focus directly and iteratively on ongoing plan formulation and plan execution, in addition to the phase of activity immediately preceding the next round of situation assessment.

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Georganta and colleagues also found that high-performing teams were less likely to follow the theoretically proposed sequence than low-performing teams, suggesting that they might be more flexible when responding to threats during the Space Alert task. For example, compared with low-performing teams, high-performing teams were more likely to execute team learning → plan formulation and team learning → plan execution, that is, instead of the theoretically predicted team learning → situation assessment sequence. Again, this suggests that team learning was used more flexibly across activity cycles by high-performing teams.

Team learning and problem-solving

The process of team learning may have resulted in different conclusions shaping ongoing adaptive action for high-performing when compared with low-performing teams. Georganta and colleagues suggest that high-performing teams may have shifted from team learning back to planning and execution, perhaps to change strategy, reframe goals, or apply lessons learned directly into action. Conversely, low-performing teams moved more frequently from team learning back to situation assessment, which may reflect a need to gain more clarity in relation to the situation and perhaps an associated failure to diagnose their situation adequately in the first instance.

While the timeframe in the analysis of group dynamics is narrow in this study (i.e., activity unfolding over 7 minutes), and while team adaptation processes may naturally change according to variation in task demands and time available, the results of the study by Georganta and colleagues suggest that flexibility in the movement through various cycles of team activity may be an important feature of successful team adaptation.

When it comes to addressing complex societal problems, including our collective response to the COVID-19 pandemic and the climate crisis, the collaborative nature of situation assessment and the time needed for effective planning can be significant, often involving many days, weeks, and months of intensive work, even for highly skilled teams. Nevertheless, an awareness of the need to learn continuously—not only updating our situational awareness but also adjusting our plans and adapting to issues that arise during plan execution—is important if we are to maintain our adaptive capacity and resilience in light of the many threats we face.