How good are ideas identified by an Automatic idea detection system?

Abstract

Online communities are an attractive source of potential ideas for products and process’. Recent advances in machine learning have made it possible to screen the vast amounts of information in online communities and automatically detect user-contributed ideas. However, it is still uncertain whether the ideas identified by such a system will also be regarded as sufficiently novel, feasible and valuable by firms who might decide to develop them further. A validation study is reported in which 200 posts were extracted from an online community using the automatic idea detection system by Christensen, Nørskov, Frederiksen and Scholderer (2016; DOI: 0.1111/caim.12202). Two company professionals evaluated the posts in terms of idea content and idea quality. The results suggest that the automatic idea detection system is sufficiently valid to be deployed for the harvesting and initial screening of innovation ideas and that the profile of the identified ideas (in terms of novelty, feasibility and value) follows the same pattern identified in studies of user ideation in general