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How to develop data-driven technology roadmaps:The integration of topic modeling and link prediction

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  • Kim, Junhan
  • Geum, Youngjung

Abstract

Technology roadmaps have been used as an important tool in strategic technology planning, due to their strong advantages in linking technologies and markets. With the rise of big data analytics, several studies have been suggested regarding data-driven technology roadmapping. However, literatures on providing a systematic method for developing data-driven technology roadmaps is surprisingly sparse. In response, this study suggests a systematic and concrete framework to develop data-driven technology roadmaps. The data-driven roadmapping is consist of three phase: layer mapping, contents mapping, and opportunity finding. The first phase, layer mapping, deals with identifying sub-layers for the technology roadmap using topic modeling. Then, contents mapping is conducted using the keyword network analysis. Third, opportunity finding is conducted to anticipate future possible innovation chances, with the help of link prediction. Our study contributes to the field by suggesting a systematic method for data-driven roadmapping, and provides data-driven evidence that helps experts to make more reasonable decision-making.

Suggested Citation

  • Kim, Junhan & Geum, Youngjung, 2021. "How to develop data-driven technology roadmaps:The integration of topic modeling and link prediction," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    • Handle: RePEc:eee:tefoso:v:171:y:2021:i:c:s0040162521004042
    DOI: 10.1016/j.techfore.2021.120972
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    References listed on IDEAS

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    Cited by:

    1. Park, Mingyu & Geum, Youngjung, 2022. "Two-stage technology opportunity discovery for firm-level decision making: GCN-based link-prediction approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    2. Yogesh K. Dwivedi & A. Sharma & Nripendra P. Rana & M. Giannakis & P. Goel & Vincent Dutot, 2023. "Evolution of Artificial Intelligence Research in Technological Forecasting and Social Change: Research Topics, Trends, and Future Directions," Post-Print hal-04292607, HAL.
    3. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    4. Just, Julian, 2024. "Natural language processing for innovation search – Reviewing an emerging non-human innovation intermediary," Technovation, Elsevier, vol. 129(C).
    5. Seo, Wonchul & Afifuddin, Mokh, 2024. "Developing a supervised learning model for anticipating potential technology convergence between technology topics," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    6. Chie Hoon Song, 2021. "Exploring and Predicting the Knowledge Development in the Field of Energy Storage: Evidence from the Emerging Startup Landscape," Energies, MDPI, vol. 14(18), pages 1-20, September.
    7. Sivarajah, Uthayasankar & Kumar, Sachin & Kumar, Vinod & Chatterjee, Sheshadri & Li, Jing, 2024. "A study on big data analytics and innovation: From technological and business cycle perspectives," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    8. Gozuacik, Necip & Sakar, C. Okan & Ozcan, Sercan, 2023. "Technological forecasting based on estimation of word embedding matrix using LSTM networks," Technological Forecasting and Social Change, Elsevier, vol. 191(C).

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