A complex adaptive perspective on learning within innovation projects

Abstract

Innovation is the lifeblood of companies, while simultaneously being one of the most difficult and elusive processes to manage. Failure rates are high – varying between six out of ten to nine out of ten – while the need to innovate is high. Departing from a real‐life case of a company, Sara Lee/Douwe Egberts, that has set learning within and from innovation projects high on the agenda, the main ideas about learning and innovation will be unfolded in the course of this article. It will be argued that most learning theories rest on a sender‐receiver model of knowledge transmission and this affects how people learn within innovation projects. A complex adaptive approach offers an alternative perspective from which one can evaluate and analyze learning and innovation processes. The most important characteristics of complex adaptive systems are non‐linearity, dynamic behavior, emergence and self‐organization. The implications of these phenomena for learning in innovation projects will be explained. The article finalizes with the preliminary findings of a multi‐agent simulation model, which explores what the underlying forces are beneath learning in innovation projects.