Predicting Consumer Perceived Risk in Online Shopping
Author Information
Author(s): Qi Lin, Xie Yunjie, Zhang Qianqian, Zhang Jian, Ma Yanhong
Primary Institution: School of Economics & Management, Beijing Information Science & Technology University, Beijing, China
Hypothesis
This study aims to predict perceived risk in different contexts by analyzing review content and website information.
Conclusion
The developed predictive model for perceived risk achieved high accuracy and identified key features affecting perceived risk in online shopping.
Supporting Evidence
- The model achieved a precision of 84%, recall of 86%, and F1 score of 85%.
- Quality, functionality, and price were identified as key features affecting perceived risk for electronic products.
- Significant differences in feature characteristics were found between high-risk samples and normal samples.
Takeaway
This study helps understand what makes people worried when shopping online, like product quality and price.
Methodology
The study used a dataset of online reviews and employed machine learning techniques, including KeyBERT-TextCNN for feature extraction and PCA-K-medoids-XGBoost for risk prediction.
Limitations
The model has limitations related to data representativeness and the comprehensiveness of feature selection.
Digital Object Identifier (DOI)
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