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Using machine learning to explore the determinants of service satisfaction with online healthcare platforms during the COVID-19 pandemic

Author

Listed:
  • Chengyu Liu

    (Shandong University)

  • Yan Li

    (Shandong University)

  • Mingjie Fang

    (Korea University Business School)

  • Feng Liu

    (Shandong University)

Abstract

This study investigates the determinants of service satisfaction with online healthcare platforms using machine learning (ML) algorithms. By training and testing eleven ML models based on data mined from a leading online healthcare platform in China, we obtained the best-performing ML algorithm for service satisfaction prediction, namely, Light Gradient Boosting Machine. Furthermore, our empirical results indicate that gifts, patient votes, popularity, fee-based consultation volume, gender, and thank-you letters positively impact service satisfaction, while the impacts of consultation volume, free consultation volume, views, waiting time, articles, physician title, and hospital level are negative. We discuss the theoretical and managerial implications.

Suggested Citation

  • Chengyu Liu & Yan Li & Mingjie Fang & Feng Liu, 2023. "Using machine learning to explore the determinants of service satisfaction with online healthcare platforms during the COVID-19 pandemic," Service Business, Springer;Pan-Pacific Business Association, vol. 17(2), pages 449-476, June.
    • Handle: RePEc:spr:svcbiz:v:17:y:2023:i:2:d:10.1007_s11628-023-00535-x
    DOI: 10.1007/s11628-023-00535-x
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    References listed on IDEAS

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    Cited by:

    1. Liu, Feng & Huang, Wanying & Zhang, Jing & Fang, Mingjie, 2024. "Corporate social responsibility in family business: Using machine learning to uncover who is doing good," Technology in Society, Elsevier, vol. 76(C).
    2. Liu, Feng & Wang, Rongping & Fang, Mingjie, 2024. "Mapping green innovation with machine learning: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 200(C).

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