The Team Scaling Fallacy: Underestimating the Declining Efficiency of Larger Teams

The Team Scaling Fallacy: Underestimating the Declining Efficiency of Larger Teams

Author

Bradley R. Staats. Katherine L. Milkman. Craig R. Fox.

Year
2012
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The Team Scaling Fallacy: Underestimating the Declining Efficiency of Larger Teams

Bradley R. Staats. Katherine L. Milkman. Craig R. Fox.. 2012. (View Paper → )

The competitive survival of many organizations depends on delivering projects on time and on budget. These firms face decisions concerning how to scale the size of work teams. Larger teams can usually complete tasks more quickly, but the advantages associated with adding workers are often accompanied by various disadvantages (such as the increased burden of coordinating efforts). We note several reasons why managers may focus on process gains when they envision the consequences of making a team larger, and why they may underestimate or underweight process losses. We document a phenomenon that we term the team scaling fallacy—as team size increases, people increasingly underestimate the number of labor hours required to complete projects. Using data from two laboratory experiments, and archival data from projects executed at a software company, we find persistent evidence of the team scaling fallacy and explore a reason for its occurrence.

Key Points:

  • As team size grows, coordination becomes exponentially more complex. Account for these coordination costs.
  • Adding more people to a project may not speed up completion proportionately. Larger teams often face more miscommunication and inefficiency, so project managers should be cautious when scaling teams with the assumption of faster delivery.
  • Focus on Integration, Not Just Task Division: While dividing tasks among more people can improve specialization, leaders must not overlook the time required to integrate those tasks into a coherent whole, especially in software projects where components need to interact seamlessly.
  • Be mindful of over estimating process gains (e.g. faster task completion) and underweighting process losses like increased coordination complexity.
  • To reduce the coordination burden, break down projects into smaller, independent modules with well-defined interfaces. This allows teams to work more autonomously, minimizing the need for constant synchronization.
  • Providing incentives for more accurate estimations can reduce optimism bias. Estimators should be encouraged to factor in potential inefficiencies and not just the best-case scenarios when planning the project timeline.
  • Historical data on similar software projects, especially regarding team size and integration challenges, should be used as a benchmark. Estimators tend to underestimate time and effort, especially as team size grows, so building on past data can help avoid this pitfall.
  • Larger teams can lead to reduced motivation due to social loafing or decreased member satisfaction, which can hurt overall project efficiency. Keeping teams smaller and more cohesive can help maintain motivation and productivity.

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