Feature mining and analysis of gray privacy products
DOI | https://doi.org/10.1108/IDD-09-2019-0063 |
Pages | 67-78 |
Date | 10 January 2020 |
Published date | 10 January 2020 |
Author | Huosong Xia,Yuting Meng,Wuyue An,Zixuan Chen,Zuopeng Zhang |
Subject Matter | Library & information science,Library & information services,Lending,Document delivery,Collection building & management,Stock revision,Consortia |
Feature mining and analysis of
gray privacy products
Huosong Xia
Wuhan Textile University, Wuhan, China and Research center of Enterprise Decision Support,
Key Research Institute of Humanities and Social Sciences in Universities of Hubei Province, Wuhan, China
Yuting Meng and Wuyue An
Wuhan Textile University, Wuhan, China
Zixuan Chen
Beijing University of Posts and Telecommunications, Beijing, China, and
Zuopeng Zhang
University of North Florida, Jacksonville, Florida, USA
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 Gray privacy products, Female underwear, Online reviews, Feature mining, Outlier, Social network analysis
Paper type Research paper
1. Introduction
Consumers are typically more inclined to buy underwear in
offline physical stores.However, a quick online search indicates
that underwear also has a large online market. For instance,
according to China textile economic information network, the
annual sales of China’s underwearmarket in 2017 is more than
100 billion, andit is growing at a rate of nearly 20 per cent every
year. In the entire market, the share of woman’s underwear
accounts for about 60 per cent with the market size exceeding
¥50bn.
This research categorizes products such as woman’s
underwear and special-purpose cosmetics as gray privacy
products because they are sensitiveto human privacy data. For
this type of product, consumers hope to buy them with high
quality and genuine brands but at the same time they are
reluctant to disclose their true preferences and privacy data.
Therefore, there exists a gap between consumers and
businesses for their mutual understanding of the attributes of
gray privacy products.
Because of the privacy concerns of gray privacy products,
consumers have limited awareness of the true characteristics of
the products. Even if they are not satisfied with the products, they
can only suffer and remain silent. The particularity of the
products creates speculative psychology for merchants, resulting
Thecurrentissueandfulltextarchiveofthisjournalisavailableon
Emerald Insight at: https://www.emerald.com/insight/2398-6247.htm
Information Discovery and Delivery
48/2 (2020) 67–78
© Emerald Publishing Limited [ISSN 2398-6247]
[DOI 10.1108/IDD-09-2019-0063]
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).
Received 12 September 2019
Revised 25 September 2019
18 October 2019
20 November 2019
Accepted 22 November 2019
67
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