Feature mining and analysis of gray privacy products
Information Discovery and Delivery
ISSN: 2398-6247
Article publication date: 3 February 2020
Issue publication date: 7 May 2020
Abstract
Purpose
Excavating valuable outlier information of gray privacy products, the purpose of this study takes the online reviews of women’s underwear as an example, explores the outlier characteristics of online commentary data, and analyzes the online consumer behavior of consumers’ gray privacy products.
Design/methodology/approach
This research adopts the social network analysis method to analyze online reviews. Based on the online reviews collected from women’s underwear flagship store Victoria’s Secret at Tmall, this study performs word segmentation and word frequency analysis. Using the fuzzy query method, the research builds the corresponding co-word matrix and conducts co-occurrence analysis to summarize the factors affecting consumers’ purchase behavior of female underwear.
Findings
Establishing a formal framework of gray privacy products, this paper confirms the commonalities among consumers with respect to their perceptions of gray privacy products, shows that consumers have high privacy concerns about the disclosure or secondary use of personal private information when shopping gray privacy products, and demonstrates the big difference between online reviews of gray privacy products and their consumer descriptions.
Originality/value
The research lays a solid foundation for future research in gray privacy products. The factors identified in this study provide a practical reference for the continuous improvement of gray privacy products and services.
Keywords
Acknowledgements
This research has been supported by the National Natural Science Foundation of China (71571139, Outlier Analytics and Model of Outlier Knowledge Management in the context of Big Data; 71871172, Model of Risk knowledge acquisition and Platform governance in FinTech based on deep learning); we deeply appreciate the suggestions from fellow members of Xia’s project team and Research center of Enterprise Decision Support, Key Research Institute of Humanities and Social Sciences in Universities of Hubei Province (DSS20180204).
Citation
Xia, H., Meng, Y., An, W., Chen, Z. and Zhang, Z. (2020), "Feature mining and analysis of gray privacy products", Information Discovery and Delivery, Vol. 48 No. 2, pp. 67-78. https://doi.org/10.1108/IDD-09-2019-0063
Publisher
:Emerald Publishing Limited
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