IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v10y2023i1d10.1057_s41599-023-02183-y.html
   My bibliography  Save this article

Evidence of the time-varying impacts of the COVID-19 pandemic on online search activities relating to shopping products in South Korea

Author

Listed:
  • Jiam Song

    (POSTECH)

  • Kwangmin Jung

    (POSTECH)

  • Jonghun Kam

    (POSTECH)

Abstract

The COVID-19 pandemic has changed the level of the received risk of the public and their social behavior patterns since 2020. This study aims to investigate temporal changes of online search activities of the public about shopping products, harnessing the NAVER DataLab Shopping Insight (NDLSI) data (weekly online search activity volumes about +1,800 shopping products) over 2017–2021. This study conducts the singular value decomposition (SVD) analysis of the NDLSI data to extract the major principal components of online search activity volumes about shopping products. Before the pandemic, the NDLSI data shows that the first principal mode (15% of variance explained) is strongly associated with an increasing trend of search activity volumes relating to shopping products. The second principal mode (10%) is strongly associated with the seasonality of monthly temperature, but in advance of four weeks. After removing the increasing trend and seasonality in the NDLSI data, the first major mode (27%) is related to the multiple waves of the new confirm cases of corona virus variants. Generally, life/health, digital/home appliance, food, childbirth/childcare shopping products are associated with the waves of the COVID-19 pandemic. While search activities for 241 shopping products are associated with the new confirmed cases of corona virus variants after the first wave, 124 and 190 shopping products are associated after the second and third waves. These changes of the public interest in online shopping products are strongly associated with changes in the COVID-19 prevention policies and risk of being exposed to the corona virus variants. This study highlights the need to better understand changes in social behavior patterns, including but not limited to e-commerce activities, for the next pandemic preparation.

Suggested Citation

  • Jiam Song & Kwangmin Jung & Jonghun Kam, 2023. "Evidence of the time-varying impacts of the COVID-19 pandemic on online search activities relating to shopping products in South Korea," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
  • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02183-y
    DOI: 10.1057/s41599-023-02183-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-023-02183-y
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/s41599-023-02183-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sarah Dryhurst & Claudia R. Schneider & John Kerr & Alexandra L. J. Freeman & Gabriel Recchia & Anne Marthe van der Bles & David Spiegelhalter & Sander van der Linden, 2020. "Risk perceptions of COVID-19 around the world," Journal of Risk Research, Taylor & Francis Journals, vol. 23(7-8), pages 994-1006, August.
    2. Yan Carrière‐Swallow & Felipe Labbé, 2013. "Nowcasting with Google Trends in an Emerging Market," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 289-298, July.
    3. Jaemin Woo & Ann L. Owen, 2019. "Forecasting private consumption with Google Trends data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(2), pages 81-91, March.
    4. Jasper Grashuis & Theodoros Skevas & Michelle S. Segovia, 2020. "Grocery Shopping Preferences during the COVID-19 Pandemic," Sustainability, MDPI, vol. 12(13), pages 1-10, July.
    5. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    6. Stiglitz, Joseph E., 2021. "The proper role of government in the market economy: The case of the post-COVID recovery," Journal of Government and Economics, Elsevier, vol. 1(C).
    7. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    8. Tan, David & Caponecchia, Carlo, 2021. "COVID-19 and the public perception of travel insurance," Annals of Tourism Research, Elsevier, vol. 90(C).
    9. Yue Teng & Dehua Bi & Guigang Xie & Yuan Jin & Yong Huang & Baihan Lin & Xiaoping An & Dan Feng & Yigang Tong, 2017. "Dynamic Forecasting of Zika Epidemics Using Google Trends," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-10, January.
    10. Fadi Makki & Paola Schietekat Sedas & Jana Kontar & Nabil Saleh & Dario Krpan, 2020. "Compliance and stringency measures in response to COVID-19: A regional study," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 4(S2), pages 15-24, December.
    11. Darougheh, Saman, 2021. "Dispersed consumption versus compressed output: Assessing the sectoral effects of a pandemic," Journal of Macroeconomics, Elsevier, vol. 68(C).
    12. Van Kien Pham & Thu Ha Do Thi & Thu Hoai Ha Le, 2020. "A study on the COVID-19 awareness affecting the consumer perceived benefits of online shopping in Vietnam," Cogent Business & Management, Taylor & Francis Journals, vol. 7(1), pages 1846882-184, January.
    13. Sheth, Jagdish, 2020. "Impact of Covid-19 on consumer behavior: Will the old habits return or die?," Journal of Business Research, Elsevier, vol. 117(C), pages 280-283.
    14. Jordan Wilcoxson & Lendie Follett & Sean Severe, 2020. "Forecasting Foreign Exchange Markets Using Google Trends: Prediction Performance of Competing Models," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 21(4), pages 412-422, October.
    15. Horvath, Akos & Kay, Benjamin & Wix, Carlo, 2023. "The COVID-19 shock and consumer credit: Evidence from credit card data," Journal of Banking & Finance, Elsevier, vol. 152(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
    2. Karaman Örsal, Deniz Dilan, 2021. "Onlinedaten und Konsumentscheidungen: Voraussagen anhand von Daten aus Social Media und Suchmaschinen," Edition HWWI: Chapters, in: Straubhaar, Thomas (ed.), Neuvermessung der Datenökonomie, volume 6, pages 157-172, Hamburg Institute of International Economics (HWWI).
    3. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
    4. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
    5. Kristina Gligorić & Arnaud Chiolero & Emre Kıcıman & Ryen W. White & Robert West, 2022. "Population-scale dietary interests during the COVID-19 pandemic," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    6. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    7. Zhongchen Song & Tom Coupé, 2023. "Predicting Chinese consumption series with Baidu," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 21(3), pages 429-463, July.
    8. Ladislav Kristoufek, 2013. "Can Google Trends search queries contribute to risk diversification?," Papers 1310.1444, arXiv.org.
    9. Christine Dauth & Julia Lang, 2024. "Continuing vocational training in times of economic uncertainty: an event-study analysis in real time," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 58(1), pages 1-23, December.
    10. Sigitas Urbonavicius & Karina Adomaviciute – Sakalauske, 2023. "Learning from Pandemic Periods: Elements of the Theory of Behavioral Transformation," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 35(2), pages 251-266.
    11. Paul Gift, 2020. "Moving the Needle in MMA: On the Marginal Revenue Product of UFC Fighters," Journal of Sports Economics, , vol. 21(2), pages 176-209, February.
    12. Olivier Gergaud & Victor Ginsburgh, 2016. "Evaluating the Economic Effects of Cultural Events," Working Papers ECARES ECARES 2016-24, ULB -- Universite Libre de Bruxelles.
    13. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
    14. Minh Hieu Nguyen & Jimmy Armoogum & Binh Nguyen Thi, 2021. "Factors Affecting the Growth of E-Shopping over the COVID-19 Era in Hanoi, Vietnam," Sustainability, MDPI, vol. 13(16), pages 1-21, August.
    15. Cruz-Cárdenas, Jorge & Zabelina, Ekaterina & Guadalupe-Lanas, Jorge & Palacio-Fierro, Andrés & Ramos-Galarza, Carlos, 2021. "COVID-19, consumer behavior, technology, and society: A literature review and bibliometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    16. Ali Zackery & Joseph Amankwah-Amoah & Zahra Heidari Darani & Shiva Ghasemi, 2022. "COVID-19 Research in Business and Management: A Review and Future Research Agenda," Sustainability, MDPI, vol. 14(16), pages 1-32, August.
    17. Jacques Bughin, 2015. "Google searches and twitter mood: nowcasting telecom sales performance," Netnomics, Springer, vol. 16(1), pages 87-105, August.
    18. Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
    19. Chien-jung Ting & Yi-Long Hsiao, 2022. "Nowcasting the GDP in Taiwan and the Real-Time Tourism Data," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 12(3), pages 1-2.
    20. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02183-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.