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Disruption in Chinese E-Commerce During COVID-19

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Title
Disruption in Chinese E-Commerce During COVID-19
Author(s)
Yuan, Yuan; Guan, Muzhi; Zhou, Zhilun; Sundong Kim; Meeyoung Cha; Jin, Depeng; Li, Yong
Publication Date
2021-03
Journal
Frontiers in Computer Science, v.3
Publisher
Frontiers Media S.A.
Abstract
The recent outbreak of the novel coronavirus (COVID-19) has infected millions of citizens worldwide and claimed many lives. This paper examines the impact of COVID-19 on Chinese e-commerce by analyzing behavioral changes observed on a large online shopping platform. We first conduct a time series analysis to identify product categories that faced the most extensive disruptions. The time-lagged analysis shows that behavioral patterns of shopping actions are highly responsive to the epidemic's development. Based on these findings, we present a consumer demand prediction method by encompassing the epidemic statistics and behavioral features of COVID-19-related products. Experimental results demonstrate that our predictions outperform existing baselines and further extend to long-term and province-level forecasts. Finally, we discuss how our market analysis and prediction can help better prepare for future pandemics by gaining extra time to launch preventive measures.
URI
https://pr.ibs.re.kr/handle/8788114/11009
DOI
10.3389/fcomp.2021.668711
ISSN
2624-9898
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Data Science Group(데이터 사이언스 그룹) > 1. Journal Papers (저널논문)
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