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Evaluation of Online Communities for Technology Foresight: Data-Driven Approach Based on Expertise and Diversity

Author

Listed:
  • Youngjun Kim

    (Samsung SDS, Seoul 05510, Korea)

  • Changho Son

    (Department of Weapon System Engineering, Korea Army Academy at Yeong-Cheon, Yeongcheon-si 770-849, Korea)

Abstract

This study proposes a framework for selecting and validating data sources for public-based technology foresight. In other words, it finds out which of the many online communities have valuable data sources. Specifically, we evaluate the usefulness of text data from online communities for technology foresight in terms of expertise and diversity. To this end, not only is a bibliographic analysis using metadata conducted, but also, topic modeling techniques for a semantic analysis of texts are utilized. As a case study, we selected 20 candidate communities where discussions and predictions related to technology are made and applied newly proposed metrics. As a contribution of this study, it is expected that it will provide a basis for public participation in technology foresight, not only leaving it to a few experts.

Suggested Citation

  • Youngjun Kim & Changho Son, 2022. "Evaluation of Online Communities for Technology Foresight: Data-Driven Approach Based on Expertise and Diversity," Sustainability, MDPI, vol. 14(20), pages 1-13, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13040-:d:939733
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    References listed on IDEAS

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    1. Lars Janzik & Christina Raasch, 2011. "Online Communities In Mature Markets: Why Join, Why Innovate, Why Share?," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 15(04), pages 797-836.
    2. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    3. Wiener, Melanie & Gattringer, Regina & Strehl, Franz, 2020. "Collaborative open foresight - A new approach for inspiring discontinuous and sustainability-oriented innovations," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    4. Zeng, Michael A., 2018. "Foresight by online communities – The case of renewable energies," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 27-42.
    5. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    6. Xenias, Dimitrios & Whitmarsh, Lorraine, 2013. "Dimensions and determinants of expert and public attitudes to sustainable transport policies and technologies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 48(C), pages 75-85.
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