Published 2018

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Publication details

Journal : Creativity and Innovation Management , vol. 27 , p. 23–31 , 2018

Publisher : Blackwell Publishing

International Standard Numbers :
Printed : 0963-1690
Electronic : 1467-8691

Publication type : Academic article

Contributors : Christensen, Kasper Knoblauch; Scholderer, Joachim; Hersleth, Stine Alm; Næs, Tormod; Kvaal, Knut; Mollestad, Torulf; Veflen, Nina; Risvik, Einar

Issue : 1

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Kjetil Aune
Chief Librarian


Online communities can be an attractive source of ideas for product and process innovations. However, innovative user‐contributed ideas may be few. From a perspective of harnessing “big data” for inbound open innovation, the detection of good ideas in online communities is a prob- lem of detecting rare events. Recent advances in text analytics and machine learning have made it possible to screen vast amounts of online information and automatically detect user‐contributed ideas. However, it is still uncertain whether the ideas identified by such systems 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 from an online home brewing commu- nity were extracted by an automatic idea detection system. Two professionals from a brewing company evaluated the posts in terms of idea content, idea novelty, idea feasibility and idea value. The results suggest that the automatic idea detection system is sufficiently valid to be deployed for the harvesting and initial screening of 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 ide- ation in general