Toward data-driven idea generation: Application of Wikipedia to morphological analysis

  • Heeyeul Kwon
  • , Yongtae Park
  • , Youngjung Geum

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

The generation of new and creative ideas is vital to stimulating innovation. Morphological analysis is one appropriate method given its objective, impersonal, and systematic nature. However, how to build a morphological matrix is a critical problem, especially in the big data era. This research focuses on Wikipedia's case-specific characteristics and well-coordinated knowledge structure and attempts to integrate the platform with morphological analysis. In details, several methodological options are explored to implement Wikipedia data into morphological analysis. We then propose a Wikipedia-based approach to the development of morphological matrix, which incorporates the data on table of contents, hyperlinks, and categories. Its feasibility was demonstrated through a case study of drone technology, and its validity and effectiveness was shown based on a comparative analysis with a conventional discussion-based approach. The methodology is expected to be served as an essential supporting tool for generating creative ideas that could spark innovation.

Original languageEnglish
Pages (from-to)56-80
Number of pages25
JournalTechnological Forecasting and Social Change
Volume132
DOIs
StatePublished - Jul 2018

Keywords

  • Big data
  • Idea generation
  • Ideation
  • Morphological analysis
  • Wikipedia

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