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

Heeyeul Kwon, Yongtae Park and Youngjung Geum

Technological Forecasting and Social Change, 2018, vol. 132, issue C, 56-80

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.

Keywords: Idea generation; Ideation; Morphological analysis; Wikipedia; Big data (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:132:y:2018:i:c:p:56-80

DOI: 10.1016/j.techfore.2018.01.009

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