Not logged in.

Quick Search - Contribution

Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title How good are ideas identified by an automatic idea detection system?
Organization Unit
Authors
  • Kasper Christensen
  • Joachim Scholderer
  • Stine Alm Hersleth
  • Tormod Naes
  • Knut Kvaal
  • Torulf Mollestad
  • Nina Veflen
  • Einar Risvik
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title Creativity and Innovation Management
Publisher Wiley-Blackwell Publishing, Inc.
Geographical Reach international
ISSN 0963-1690
Volume 27
Number 1
Page Range 23 - 31
Date 2018
Abstract Text 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 problem 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 community 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 ideation in general.
Related URLs
Digital Object Identifier 10.1111/caim.12260
Other Identification Number merlin-id:20056
PDF File Download from ZORA
Export BibTeX
EP3 XML (ZORA)