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

Abstract

This 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.

Dynamics of innovation in engineering design teams: complex network approach

“…The first part comprises three projects related to innovation in teams and delve into various aspects of team composition, structure, and dynamics to gain insights into optimizing team performance in innovation contexts. In the first project 1 , I try to address two important questions. Firstly, what is the ideal composition of innovating agents that leads to high performance in innovation?…”

“…Undoubtedly, theoretical studies that compare the outcomes of individual performance to interactive group performance contribute to our understanding of the theoretical dynamics, conditions, and mechanisms that determine the varying levels of complexity requiring diverse levels of collective search, imitation, and adoption (1). By examining the behavioral tendencies, motivations, and conditioning factors that encourage interaction among participants, we can gain insights into the actual behaviors of collective agents when confronted with complexity.…”

“…Assessment of innovation in technological life cycles is valuable for a variety of commercial, economic, and social reasons. 1 Many researchers have utilized technological life cycles to study innovation structures (124), patterns of knowledge development and diffusion (125; 126; 127; 128) and technological sustainability (129). However, it is not easy to identify innovation rate, and multiple reasons hinders prediction of future trajectory of technological life cycles and forecasting patterns of innovation.…”

“…How does the coevolution and competition of two innovations influence each other within the complex ecosystem of sharing economy platforms? Specifically, how do sharing economy platforms, such as short-term rentals, contribute to enhancing locals' experiences of neighborhood interactions and experiences?1 While acknowledging that the chosen topics and projects may not comprehensively address all the open questions surrounding the interplay of innovation and technological development, this article aims to connect various theories together. By doing so, it fills research gaps and partially reinforces existing theories, providing valuable insights that can guide team designers and policy makers in their decision-making processes.Approaches to Research Questions on Team Design and InnovationThis section of the article focuses on addressing questions regarding the composition and configuration of innovating agents in engineering design teams, from the perspectives and considerations of team designers.…”