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Analysis of Changes in In-Store and Online Shopping Frequencies Due to the COVID-19 Pandemic: A Case Study from Bahrain

Author

Listed:
  • Eman A. Algherbal

    (Department of Civil and Environmental Engineering, College of Design and Built Environment, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia)

  • Hussam I. Hijazi

    (Department of Civil and Environmental Engineering, College of Design and Built Environment, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia)

  • Hassan M. Al-Ahmadi

    (Department of Civil and Environmental Engineering, College of Design and Built Environment, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia
    Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia)

  • Muhammad Abdullah

    (Department of Civil and Environmental Engineering, College of Design and Built Environment, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia
    Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia)

Abstract

Online shopping (e-shopping) has been growing steadily in recent years; however, the COVID-19 pandemic resulted in a sudden increase in this growth. This study compares the in-store shopping and e-shopping frequencies within three distinct periods, i.e., before, during, and after the COVID-19 pandemic. It further investigates the frequencies and determinants of e-shopping across the three periods. The data on the in-store shopping and e-shopping frequencies for four different product categories, i.e., grocery, household essentials, electronics, and clothes, were collected through an online questionnaire in Bahrain, resulting in a total of 401 valid responses. Wilcoxon signed-rank tests were conducted to compare the frequencies of in-store shopping and e-shopping within the three periods as well as the frequencies of e-shopping across the three periods. The effects of the determinants of e-shopping were evaluated using chi-square tests. The results revealed that e-shopping experienced a temporary surge during the COVID-19 pandemic, returning to pre-pandemic levels afterward. The frequency of e-shopping varied across product categories and periods, and e-shopping during the pandemic was higher than before. However, no significant difference was found between the periods during and after the pandemic. This study provides insights for urban and transport planners regarding the frequencies and determinants of e-shopping behavior in the context of pandemics.

Suggested Citation

  • Eman A. Algherbal & Hussam I. Hijazi & Hassan M. Al-Ahmadi & Muhammad Abdullah, 2024. "Analysis of Changes in In-Store and Online Shopping Frequencies Due to the COVID-19 Pandemic: A Case Study from Bahrain," Sustainability, MDPI, vol. 16(12), pages 1-17, June.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:12:p:4996-:d:1413002
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    References listed on IDEAS

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    1. Yu Ding & Huapu Lu, 2017. "The interactions between online shopping and personal activity travel behavior: an analysis with a GPS-based activity travel diary," Transportation, Springer, vol. 44(2), pages 311-324, March.
    2. Wadud, Zia & Chen, Danlei, 2018. "Congestion impacts of shopping using vehicle tracking data," Journal of Transport Geography, Elsevier, vol. 70(C), pages 123-130.
    3. Jesse W.J. Weltevreden & Ton Van Rietbergen, 2007. "E‐Shopping Versus City Centre Shopping: The Role Of Perceived City Centre Attractiveness," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 98(1), pages 68-85, February.
    4. Lee, Richard J. & Sener, Ipek N. & Mokhtarian, Patricia L. & Handy, Susan L., 2017. "Relationships between the online and in-store shopping frequency of Davis, California residents," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 40-52.
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