Design teams and industry life cycles: The interplay of innovation and complexity

Abstract

AbstractThis paper studies how innovation teams can be optimally configured to yield the best possible performance at different stages of a certain technology's life cycle, which correspond to different levels of environmental complexity. To conduct our analysis, we have employed computational simulations of communities searching NK landscapes at varying levels of complexity. We studied how the relative proportion of exploring agents to exploiting agents in a community impacts the evolution of scores over time, and conducted additional investigations into the role of specialization (i.e., the agents' propensity to take their preferred action) and density (i.e., the expected width of social groups within the community).We discover that majority‐explorer teams are to be preferred when complexity is high and over the long run, whereas majority‐exploiter teams are more effective in the short run and at low complexities. Furthermore, we show that higher levels of specialization yield better results at higher complexities, and that majority‐explorer teams benefit the most from higher levels of density. We conclude that different team compositions are to be preferred at different stages of maturity, and that selecting a time horizon for operations is of crucial importance when designing an innovation team.