Discover how communication networks shape team performance. Learn how network structure, tie strength, and positioning drive coordination success.
What if the structure of how your team communicates matters more than who is on the team itself?
Communication networks are the hidden architecture determining how information flows and work gets coordinated within teams. Research reveals that the structure of these networks—not just individual talent—profoundly impacts team performance, innovation, and knowledge sharing. Understanding how information circulates through your team provides a critical lever for enhancing effectiveness.
Every team operates within an invisible architecture. Team members don't communicate equally with everyone. Instead, distinct patterns emerge: some individuals become central information hubs, others bridge isolated subgroups, and still others operate on the periphery with limited information access.
The paradox is striking: organizations invest heavily in communication tools yet struggle with coordination failures and fragmented knowledge. Meanwhile, the actual structure of who communicates with whom remains largely invisible and unexamined.
Research consistently demonstrates that communication network structure predicts team performance as reliably as individual member abilities. This means how information moves through your team may matter as much as the talent you've assembled.
Network centrality measures how critical specific individuals are to information flow. Degree centrality reflects how many people someone knows—their breadth of connection. Betweenness centrality captures whether someone bridges otherwise disconnected subgroups, serving as an information gatekeeper. Closeness centrality indicates how quickly someone can reach everyone else.
Empirical Evidence: A 2023 laboratory study with 41 three-person teams found that groups allowed to select their central member performed significantly better, committing 8.44 errors on average versus 13.61 errors for randomly assigned coordinators—a difference of 5.17 errors (38% improvement, Cohen's d = 0.81, large effect). Teams that chose their central member based on communication activity during training succeeded because the coordinator better understood information flow patterns.
For simple, well-defined tasks, centralized networks with a clear coordinator can enhance efficiency. However, for complex tasks requiring distributed expertise, decentralized networks where members coordinate directly often outperform centralized structures. The key finding: central coordinators must have relevant expertise—otherwise they become bottlenecks.
Strong ties (frequent, emotionally close relationships) are efficient for transferring complex information requiring deep understanding. However, they can create silos where information circulates freely within subgroups but rarely crosses between them.
Weak ties (infrequent, professional relationships) serve a different function: they expose individuals to novel information and diverse perspectives. Research across innovation, career advancement, and scientific discovery shows that weak ties are disproportionately responsible for introducing genuinely new ideas.
Empirical Evidence: A 2022 analysis of scientific collaboration networks using the DBLP bibliography database found that scientists with weaker ties to collaborators had higher h-indices (citation impact) than those with only strong ties. Teams connected by weak ties created more cited publications because diversity of expertise introduced novel ideas. Meanwhile, tightly connected research groups produced less creative work due to information redundancy.
Brokerage—occupying a position that bridges different groups—is particularly powerful. Brokers who actively synthesize ideas from different circles catalyze innovation. Those who hoard information become bottlenecks.
High-performing teams appear to benefit from a balanced combination: strong ties for coordination on day-to-day work, complemented by strategic weak ties that introduce fresh perspectives.
Centralized networks concentrate communication through a central coordinator. All information flows to the center, is processed, and redistributed.
Advantages:
Information gets integrated centrally
Standardized decisions happen rapidly
Clear coordination authority
Disadvantages:
Bottlenecks when complexity exceeds coordinator capacity
Peripheral members' expertise remains untapped
Less resilient if the central node fails
Decentralized networks distribute communication authority, enabling direct connections between team members.
Advantages:
Problems solved using distributed expertise
Faster local decision-making
More resilient if any single person is unavailable
Disadvantages:
Information may become fragmented
Coordination requires more total communication
Individuals can suffer information overload
Empirical Evidence: A 2024 study of 720 engineering project team members found that under conditions of weak tie strength, centralized networks yielded 23% higher performance on technical tasks. However, under conditions of strong tie strength, decentralized networks demonstrated superior performance (ANOVA results, p < 0.05). The optimal structure depends on the quality of relationships, not just the architecture alone.
Communication structure determines how teams coordinate and decide. Empirical Evidence: A 2021 study of 542 employees across 71 teams found that leader network centrality positively predicted team performance in larger teams (15+ members) but negatively in smaller teams (5-8 members). Small teams benefit from distributed communication where all voices are heard. Large teams benefit from a clear coordinator managing information flow.
Network density—the proportion of possible connections that actually exist—strongly predicts knowledge sharing. Higher density creates alternative information paths, preventing critical knowledge from getting trapped. However, it also increases overall communication volume.
Empirical Evidence: Analysis of communication networks found that 88% of a worker's week is spent communicating, with significant time managing communication itself. Teams with streamlined communication networks are 25% more productive than those with unclear communication architecture. High-density networks risk "collaboration overload."
The most innovative teams combine three elements: (1) strong ties within core collaboration groups enabling coordination; (2) strategic weak ties to external sources exposing teams to novel ideas; and (3) bridging structures enabling cross-group synthesis.
Empirical Evidence: Research across 37,000 software development projects found that developer exposure to topically diverse projects through weak interactions was a stronger predictor of future innovation than involvement intensity in strong collaborations. Diversity of ideas matters more than collaboration intensity.
Empirical Evidence: Studies on communication overload found that when team members are inundated with messages across multiple platforms, information processing quality declines. Teams managing communication through clear norms—designated channels, asynchronous-first communication, and explicit prioritization—demonstrate significantly better performance metrics.
Use social network analysis to map actual communication:
Who communicates with whom and how frequently
Which individuals occupy central positions
Where bottlenecks or silos exist
Balance between strong and weak ties
Whether informal networks align with formal structure
Rather than pursuing a single optimal structure:
Routine tasks: More centralized networks enhance efficiency
Complex tasks: Decentralized networks enable better problem-solving
Innovation tasks: Deliberately cultivate weak ties and bridging structures
Distributed teams: Acknowledge temporal brokers and develop redundant communication paths
Leaders influence network structure through:
Clear channels for different purposes
Realistic response time expectations
Transparent decision-making processes
Shared platforms reducing gatekeeper dependence
Trust-building practices beyond task requirements
Target ranges for network density:
30-50% density: Balanced connectivity reducing redundancy while maintaining collaboration
Below 30%: Risks fragmentation and isolated members
Above 70%: Risks collaboration overload and diminishing returns
Communication networks represent one of the most powerful yet invisible levers for enhancing team performance. How information flows, where bottlenecks form, and how diverse perspectives circulate determine outcomes as reliably as any other variable.
The research is clear: structure matters. Empirical evidence consistently demonstrates that groups choosing their central coordinator perform 38% better than those with randomly assigned coordinators. Teams with balanced tie strength experience higher innovation. Organizations with streamlined communication networks are 25% more productive.
Effective leadership increasingly requires the ability to see and shape these hidden networks. By developing visibility into actual communication patterns, matching network structure to task requirements, cultivating both strong ties and strategic weak ties, and establishing communication norms that reduce overload, leaders unlock team performance potential that would otherwise remain latent.
Organization Learning Labs offers diagnostic assessments designed to reveal actual communication patterns and provide evidence-based strategies for reshaping networks to enhance performance, coordination, and innovation.
Balkundi, P., & Harrison, D. A. (2006). Ties, leaders, and time in teams: Strong inference about network structure's effects on team viability and performance. Academy of Management Journal, 49(1), 49-68.
Guo, J., Argote, L., Kush, J., & Park, J. (2023). Communication networks and team performance: Selecting members to network positions. Frontiers in Psychology, 14, 1141571.
Mell, J. N., Cropanzano, R., Dechurch, L. A., & Lyons, J. (2021). Temporal brokerage in global teams and its impact on productivity and engagement. Organization Science, 32(2), 343-373.
Zhang, J., Tao, D., Wang, X., Wang, P., Li, T., & Ma, Y. (2024). Unveiling the impact of communication network on engineering project team performance: The interplay of centralization and tie strength. Frontiers in Psychology, 15, 1104745.
Fronczak, A., Mrowinski, M. J., & Fronczak, P. (2022). Scientific success from the perspective of the strength of weak ties. Nature Scientific Reports, 12, 7511.
Grammarly. (2024). The 2024 State of Business Communication Report.
Cross, R., et al. (McKinsey). Beyond Collaboration Overload. Organizational Dynamics.
Comments