Reaping What You Sow: A Multilevel Investigation of Allocation From a Pool of Workers to Teams

Organizations commonly face the task of allocating workers to mutually exclusive teams from finite worker pools-a process called seeding. The approach an organization takes to seeding affects within-team and between-team distributions of performance or other outcomes. Substantial prior research explains the effects of combinations on team performance, but little is known about between-team combinations. I extend prior theory to a higher level of analysis, elaborating on the nature and function of between-team combinations on organization-level performance. I use a simulation method to identify seeding approaches that can maximize organizational outcomes in various contexts. Results uncover conditions under which the seeding approach is irrelevant to outcomes, instances where random assignment outperforms intentional seeding, and instances where particular approaches produce the most favorable outcomes. I discuss the implications of multilevel combinations for theory and practice.