The Influence of Significant Public Health Events on Offline Consumption and Its Pathways

Baile Lu , Kewei Zhou , Shuai Hao , La Ta , Hongyan Dai , Weihua Zhou

Journal of Systems Science and Systems Engineering ›› : 1 -22.

PDF
Journal of Systems Science and Systems Engineering ›› : 1 -22. DOI: 10.1007/s11518-024-5600-5
Article

The Influence of Significant Public Health Events on Offline Consumption and Its Pathways

Author information +
History +
PDF

Abstract

In the face of a significant public health event, consumers may either increase their panic buying or decrease their willingness to make purchases. This study focuses on the impact of a significant public health event on offline store sales and consumer consumption, utilizing data from chain convenience stores in Hefei and Wuhu during early 2019 and early 2020 in China. Employing a difference-in-differences model, the study investigates the effect of the significant public health event outbreak on weekly store sales, order numbers, and consumer consumption in terms of product quantities, transaction amount, average amount per order, and transaction frequency. Different from prior literature that finds hoarding behavior of consumers online, the findings of this paper indicate a significant reduction in stores’ offline weekly sales and order numbers, as well as consumers’ offline weekly consumption across the four dimensions, as a result of the significant public health event outbreak. Additionally, employing a mediation model, the study explores the pathway of population mobility through which the significant public health event adversely affects offline consumption. Furthermore, subset analysis is conducted for stores located in different areas and consumers with varying characteristics, revealing that the aforementioned conclusions predominantly apply to stores situated in office areas and residential areas, as well as consumers with either no apparent preference for different product categories or a noticeable preference for food.

Keywords

Significant public health event / offline consumption / population mobility / mediating effect

Cite this article

Download citation ▾
Baile Lu, Kewei Zhou, Shuai Hao, La Ta, Hongyan Dai, Weihua Zhou. The Influence of Significant Public Health Events on Offline Consumption and Its Pathways. Journal of Systems Science and Systems Engineering 1-22 DOI:10.1007/s11518-024-5600-5

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Andersen A L, Hansen E T, Johannesen N, Sheridan A. Consumer responses to the COVID-19 crisis: Evidence from bank account transaction data. The Scandinavian Journal of Economics, 2022, 124(4): 905-929.

[2]

Badr H S, Du H, Marshall M, Dong E, Squire M M, Gardner L M. Association between mobility patterns and COVID-19 transmission in the USA: A mathematical modelling study. The Lancet Infectious Diseases, 2020, 20(11): 1247-1254.

[3]

Baker S R, Farrokhnia R A, Meyer S, Pagel M, Yannelis C. How does household spending respond to an epidemic? Consumption during the 2020 COVID-19 pandemic. The Review of Asset Pricing Studies, 2020, 10(4): 834-862.

[4]

Chen H, Qian W, Wen Q. The impact of the COVID-19 pandemic on consumption: Learning from high-frequency transaction data. AEA Papers and Proceedings, 2021, 111: 307-311.

[5]

Fang H, Wang L, Yang Y. Human mobility restrictions and the spread of the Novel Coronavirus (2019-nCoV) in China. Journal of Public Economics, 2020, 191: 104272.

[6]

Gao X, Shi X, Guo H, Liu Y. To buy or not buy food online: The impact of the COVID-19 epidemic on the adoption of e-commerce in China. PloS One, 2020, 15(8): e0237900.

[7]

Glaeser E L, Jin G Z, Leyden B T, Luca M. Learning from deregulation: The asymmetric impact of lock-down and reopening on risky behavior during COVID-19. Journal of Regional Science, 2021, 61(4): 696-709.

[8]

Han B R, Sun T, Chu L Y, Wu L. COVID-19 and e-commerce operations: Evidence from Alibaba. Manufacturing & Service Operations Management, 2022, 24(3): 1388-1405.

[9]

Hu S, Xiong C, Yang M, Younes H, Luo W, Zhang L. A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic. Transportation Research Part C: Emerging Technologies, 2021, 124: 102955.

[10]

Huang Y, Lu X, Xu B. COVID-19 and intercity consumption flow: From the perspectives of urban consumption function and industrial digitalization. Journal of Management Sciences in China, 2023, 26(5): 249-270.

[11]

Hwang M, Park S. The impact of Walmart supercenter conversion on consumer shopping behavior. Management Science, 2016, 62(3): 817-828.

[12]

Keane M, Neal T. Consumer panic in the COVID-19 pandemic. Journal of Econometrics, 2021, 220(1): 86-105.

[13]

Kraemer M U, Yang C H, Gutierrez B, Wu C H, Klein B, Pigott D M, et al. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 2020, 368(6490): 493-497.

[14]

Li L Y, Wu J T. The influence and formation mechanism of COVID-19 epidemic on residents’ consumption behavior. Consumer Economics, 2020, 36(3): 19-26.

[15]

Liu H, Di J, Wang R. The research of the impact of COVID-19 on household consumption. Statistical Research, 2022, 39(5): 38-48.

[16]

Liu J, Yao Y, Hong L. The impact of population mobility on the level of COVID-19 in China: Based on spatial interaction model and SEIR model. Social Sciences Review, 2022, 37(4): 66-71.

[17]

Luo L, Wang Y, Liu H. COVID-19 personal health mention detection from tweets using dual convolutional neural network. Expert Systems with Applications, 2022, 200: 117139.

[18]

Meyer B D. Natural and quasi-experiments in economics. Journal of Business & Economic Statistics, 1995, 13(2): 151-161.

[19]

Moulton B R. An illustration of a pitfall in estimating the effects of aggregate variables on micro units. The Review of Economics and Statistics, 1990, 72(2): 334-338.

[20]

Peng Z, Li M, Wang Y, Ho G T. Combating the COVID-19 infodemic using prompt-based curriculum learning. Expert Systems with Applications, 2023, 229: 120501.

[21]

Tan S, Lai S, Fang F, Cao Z, Sai B, Song B, Lu X. Mobility in China, 2020: A tale of four phases. National Science Review, 2021, 8(11): nwab148.

[22]

Tucker C E, Yu S (2020). The early effects of Coronavirus-related social distancing restrictions on brands. Available at SSRN 3566612.

[23]

Wang G. Stay at home to stay safe: Effectiveness of stay-at-home orders in containing the COVID-19 pandemic. Production and Operations Management, 2022, 31(5): 2289-2305.

[24]

Zellner A. An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association, 1962, 57(298): 348-368.

[25]

Zellner A. Estimators for seemingly unrelated regression equations: Some exact finite sample results. Journal of the American Statistical Association, 1963, 58(304): 977-992.

[26]

Zhu Y, Xie J, Huang F, Cao L. The mediating effect of air quality on the association between human mobility and COVID-19 infection in China. Environmental Research, 2020, 189: 109911.

AI Summary AI Mindmap
PDF

255

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/