The Hidden Patterns of Team Dynamics: What Group Formation Tells Us About Employee Retention

The next time you’re back @ the office from this holiday break, pay attention to how people naturally gather and disperse. You might notice something fascinating: large groups tend to fragment into smaller ones, while certain small, established teams remain remarkably stable. This seemingly simple observation actually reveals profound insights about organizational dynamics and employee retention.

Recent research by Associate Professor Lacopo Lacaponi at Northeastern University London from the Network Science Institute has uncovered patterns in group formation that have significant implications for how we think about team structure and employee turnover in organizations.

The “Time-Size Paradox” in Team Dynamics

Lacopini’s research revealed two critical factors that influence group stability:

1. The duration of membership
2. Group size.

In organizational terms, this translates into: employees are more likely to stay with their teams the longer they’ve been part of them, but larger teams face greater risks of fragmentation and turnover.

This phenomenon, which might be called the “Time-Size Paradox,” helps explain why:

• Small, long-standing teams often show remarkable stability
• Large departments frequently experience higher turnover rates
• Start-up teams that survive their first year tend to develop strong cohesion
• Mega-departments often naturally subdivide into smaller, informal working groups

The “Long-Gets-Longer” Effect

One of the most interesting findings from Iacopini’s research is what he terms the “long-gets-longer” effect: the longer people stay in a group, the more likely they are to continue staying. In the workplace, this manifests as increased loyalty and reduced turnover risk among longer-tenured employees.

This effect has profound implications for talent retention strategies:

1. The critical early period: New employees are at highest risk of departure, suggesting the importance of robust onboarding and early engagement
2. Investment compounds: Time invested in team building early on pays increasing dividends as team tenure grows
3. Stability breeds stability: Teams that maintain core membership tend to retain new members more effectively

The Size Factor

Perhaps most relevant to modern organizations is the finding that larger groups have higher probabilities of member departure. This helps explain why:

• Giant departments often struggle with retention.
• Small, focused teams tend to maintain stronger cohesion.
• Massive organizational structures frequently face collaboration challenges.
• Breaking larger units into smaller teams often improves stability.

ONA Graph by Leopoldo Torres Azcona, LET Consulting Partner, 2024

Practical Applications for Organizations

Understanding these dynamics enables organizations to make more informed decisions about team structure and retention strategies:

1. Optimal Team Sizing. Instead of creating large departments, consider structuring work around smaller, interconnected teams. This approach aligns with the natural tendency for groups to split at certain size thresholds.
2. Strategic Onboarding. Knowing that early tenure is most vulnerable to turnover, organizations can focus retention efforts on the critical first few months of employment.
3. Team Stability Management. Rather than frequently reshuffling teams, allow stable groups to build longer tenure together, capitalizing on the “long-gets-longer” effect.
4. Growth Planning. When scaling departments, plan for natural breakpoint thresholds where sub-teams might need to form to maintain stability.

The Data-Driven Advantage

These insights demonstrate the power of applying #NetworkScience to organizational dynamics. By understanding their network patterns underlying group behavior, leaders can make more informed decisions about:

• Team structure and size
• Growth planning
• Retention strategies
• Organizational design

Looking Forward

As distributed organizations continue to evolve and adapt to new ways of working, understanding these fundamental patterns of group dynamics becomes increasingly crucial. The ability to predict and manage team stability based on size and tenure can provide a significant competitive advantage in talent retention and organizational effectiveness.

For leaders and HR professionals, these findings suggest several key considerations:

1. Review current team structures through the lens of size and stability
2. Evaluate retention patterns against team tenure data.
3. Consider restructuring large departments into smaller, more stable units.
4. Design onboarding programs that accelerate team integration
5. Monitor team size growth and plan for natural breaking points

Modern organizations face unprecedented challenges in maintaining team cohesion and retaining talent. By understanding and working with natural patterns of group formation and stability, rather than against them, organizations can build more resilient and effective team structures. By connecting the dots between network science and organizational behavior, we’re helping organizations build more stable, effective, and resilient team structures.

More information on how our ONA tool works.

At LET we offer tools and methodologies to map out the ‘truest’ structure of an organization, from hosting services like identifying advocates (opinion leaders) to reducing employee churn and optimizing knowledge and product diffusion. Designing teams with diversity, size and expertise to be the most effective for specific tasks. Overall network science tools are indispensable in management and business, enhancing productivity and boosting innovation within organizations.

About the author: Leopoldo Torres Azcona

Leopoldo Torres Azcona is a Change Analytics and People Insights Advisor at LET Consulting Partner and a PhD candidate in Network Science. He specializes in Organizational Network Analysis and workplace dynamics, leveraging Network Science to guide leaders in adopting AI and analytics solutions.

References

• Iacopini, I., Karsai, M., & Barrat, A. (2024). The temporal dynamics of group interactions in higher-order social networks. Nature Communications, 15(1), 1–11. https://doi.org/10.1038/s41467-024-50918-5
• Barabási, A.-L., & P?3sfai, M. (2016). Network Science. Cambridge University Press