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Proposing Machine Learning Models Suitable for Predicting Open Data Utilization

Author

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  • Junyoung Jeong

    (Graduate School of Management of Technology, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea)

  • Keuntae Cho

    (Graduate School of Management of Technology, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea
    Department of Systems Management Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea)

Abstract

As the digital transformation accelerates in our society, open data are being increasingly recognized as a key resource for digital innovation in the public sector. This study explores the following two research questions: (1) Can a machine learning approach be appropriately used for measuring and evaluating open data utilization? (2) Should different machine learning models be applied for measuring open data utilization depending on open data attributes (field and usage type)? This study used single-model (random forest, XGBoost, LightGBM, CatBoost) and multi-model (stacking ensemble) machine learning methods. A key finding is that the best-performing models differed depending on open data attributes (field and type of use). The applicability of the machine learning approach for measuring and evaluating open data utilization in advance was also confirmed. This study contributes to open data utilization and to the application of its intrinsic value to society.

Suggested Citation

  • Junyoung Jeong & Keuntae Cho, 2024. "Proposing Machine Learning Models Suitable for Predicting Open Data Utilization," Sustainability, MDPI, vol. 16(14), pages 1-23, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:14:p:5880-:d:1432443
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    References listed on IDEAS

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