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This study predicts e-commerce perceived risk using review content and website data. Key features like quality, safety, and price significantly influence user risk perception.

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Area of Science:

  • E-commerce
  • Consumer Behavior
  • Data Science

Background:

  • E-commerce faces challenges like content homogenization and high user perceived risk.
  • Analyzing online reviews and website data is crucial for understanding and mitigating these risks.

Purpose of the Study:

  • To develop a predictive model for perceived risk in e-commerce.
  • To identify key features influencing perceived risk across different product categories.

Main Methods:

  • Utilized KeyBERT-TextCNN for thematic feature extraction from 262,752 online reviews.
  • Combined thematic features with product/merchant data.
  • Employed PCA-K-medoids-XGBoost for predictive modeling.

Main Results:

  • Identified 11 key features influencing perceived risk.
  • Achieved high performance with 84% precision, 86% recall, and 85% F1 score.
  • Quality, functionality, and price are critical for electronics; skin safety is paramount for skincare.

Conclusions:

  • The developed model effectively predicts perceived risk in e-commerce.
  • Feature importance varies by product type, highlighting the need for context-specific analysis.
  • Significant differences exist between high-risk and normal samples, informing risk mitigation strategies.