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

Chengyu Liu (), Yan Li (), Mingjie Fang () and Feng Liu ()
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Chengyu Liu: Shandong University
Yan Li: Shandong University
Mingjie Fang: Korea University Business School
Feng Liu: Shandong University

Service Business, 2023, vol. 17, issue 2, No 1, 449-476

Abstract: 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.

Keywords: Service satisfaction; Machine learning; Online healthcare platforms; COVID-19 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11628-023-00535-x

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