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

Author

Listed:
  • Kwon, Heeyeul
  • Park, Yongtae
  • Geum, Youngjung

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.

Suggested Citation

  • Kwon, Heeyeul & Park, Yongtae & Geum, Youngjung, 2018. "Toward data-driven idea generation: Application of Wikipedia to morphological analysis," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 56-80.
    • Handle: RePEc:eee:tefoso:v:132:y:2018:i:c:p:56-80
    DOI: 10.1016/j.techfore.2018.01.009
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    References listed on IDEAS

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    1. Ward, Thomas B., 2004. "Cognition, creativity, and entrepreneurship," Journal of Business Venturing, Elsevier, vol. 19(2), pages 173-188, March.
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    5. Geum, Youngjung & Park, Yongtae, 2016. "How to generate creative ideas for innovation: a hybrid approach of WordNet and morphological analysis," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 176-187.
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    Cited by:

    1. Richarz, Jan & Wegewitz, Stephan & Henn, Sarah & Müller, Dirk, 2023. "Graph-based research field analysis by the use of natural language processing: An overview of German energy research," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    2. Lijie Feng & Yuxiang Niu & Zhenfeng Liu & Jinfeng Wang & Ke Zhang, 2019. "Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT," Sustainability, MDPI, vol. 12(1), pages 1-35, December.
    3. Motohashi, Kazuyuki & Zhu, Chen, 2023. "Identifying technology opportunity using dual-attention model and technology-market concordance matrix," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    4. Lijie Feng & Yilang Li & Zhenfeng Liu & Jinfeng Wang, 2020. "Idea Generation and New Direction for Exploitation Technologies of Coal-Seam Gas through Recombinative Innovation and Patent Analysis," IJERPH, MDPI, vol. 17(8), pages 1-21, April.
    5. Wang, Jinfeng & Zhang, Zhixin & Feng, Lijie & Lin, Kuo-Yi & Liu, Peng, 2023. "Development of technology opportunity analysis based on technology landscape by extending technology elements with BERT and TRIZ," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    6. Lee, Changyong & Jeon, Daeseong & Ahn, Joon Mo & Kwon, Ohjin, 2020. "Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database," Technovation, Elsevier, vol. 96.
    7. Mingyu Park & Youngjung Geum, 2021. "On the data-driven generation of new service idea: integrated approach of morphological analysis and text mining," Service Business, Springer;Pan-Pacific Business Association, vol. 15(3), pages 539-561, September.

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