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Publisert 2018


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

Utgiver : Blackwell Publishing

Internasjonale standardnummer :
Trykt : 0963-1690
Elektronisk : 1467-8691

Publikasjonstype : Vitenskapelig artikkel

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

Sak : 1

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Kjetil Aune


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