Team recommendation in open innovation networks

Abstract

Open Innovation has become an important new paradigm for incorporating external knowledge and sources in the innovation process of organizations. Besides other discussed arguments the resulting large size of innovator networks suggests that algorithmic approaches for team recommendation may be needed in that scenario. The current work identifies the related difficulties and thoroughly investigates aspects entities for the problem of team recommendation. Based on that, we develop a meta model which allows to instantiate and integrate most of the vast number of the existing socio-/psychological models on optimal team composition. This meta model is necessary for operationalizing our intended team recommendation approach.