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Data-Driven Prioritization of User Requirements in Health E-Commerce: An Explainable Machine Learning Study

Fanyong Meng & Yincan Jia

Journal of Theoretical and Applied Electronic Commerce Research2026https://doi.org/10.3390/jtaer21040104article
AJG 1
Weight
0.50

What the paper says

The rapid expansion of mobile healthcare (mHealth) applications has transformed health-related e-commerce, creating new challenges for understanding and responding to user needs. This study proposes a data-driven framework to systematically identify and prioritize unmet user requirements from negative reviews of Chinese mHealth applications. Using a dataset of 31,124 user reviews collected between 2019 and 2025, the framework integrates sentiment analysis, topic modeling, and machine learning regression to uncover six key areas of user concern and examine their temporal evolution. Among several predictive models linking user concerns to app ratings, the k-nearest neighbors (KNN) model demonstrated superior performance. Subsequent SHAP-based interpretability analysis reveals that account authentication, system accessibility, and application stability have the most significant impact on user ratings, highlighting the critical roles of trust and technical reliability in health e-commerce. This research not only provides actionable insights for platform governance but also contributes a generalizable methodology for leveraging user-generated content to inform evidence-based management and policy decisions in mobile digital services.

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https://doi.org/https://doi.org/10.3390/jtaer21040104

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@article{fanyong2026,
  title        = {{Data-Driven Prioritization of User Requirements in Health E-Commerce: An Explainable Machine Learning Study}},
  author       = {Fanyong Meng & Yincan Jia},
  journal      = {Journal of Theoretical and Applied Electronic Commerce Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.3390/jtaer21040104},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.