IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v191y2025ics0965856424003653.html
   My bibliography  Save this article

Willingness to limit “panic buying” during the COVID-19 crisis

Author

Listed:
  • Calderón, Oriana
  • Amaral, Julia C.
  • Holguín-Veras, José

Abstract

The research in this paper seeks to quantify the potential of a novel initiative to mitigate “panic buying” through appeals from “trusted change agents” (TCAs) that ask individuals to limit their purchases of critical supplies. TCAs are agents involved in disaster response efforts, such as local leaders and representatives of relief groups, that are deemed to be trustworthy by various segments of the population. To assess the effectiveness of these appeals, the authors conducted a survey of residents in the United States (U.S.) to characterize the respondents’ purchases of critical supplies made before, during, and after the pandemic. In addition, stated preference data were gathered about whether, and the extent to which, the respondents would react to a request from TCAs to limit their purchases of critical supplies. The results of the survey were used to: (i) assess the level of trust the respondents have in the various agents considered; (ii) estimate the expected impacts of TCAs’ requests in terms of changes in respondents’ purchasing behaviors; and (iii) estimate a Structural Equation Model-Multiple Indicators Multiple Causes (SEM-MIMIC) model that expresses the respondents’ willingness to limit purchases as a function of the socioeconomic attributes of the respondents. The research demonstrated that TCAs have substantial influence, with 58 % of respondents willing to limit purchases based on TCAs’ appeals. Firefighters, Emergency Responders, and Health Officials are the most trusted agents, with Firefighters having the highest expected impact (22.2 %). Joint appeals amplify impact, e.g., Firefighters combined with Local Government and Emergency Responders have an impact of 33.3 %. The SEM-MIMIC model also showed that socioeconomic variables affect trust in TCAs. The results of the research provide a pathway to mitigate “panic buying” and reduce the associated shortages. Based on the results obtained, the authors discuss the corresponding policy implications.

Suggested Citation

  • Calderón, Oriana & Amaral, Julia C. & Holguín-Veras, José, 2025. "Willingness to limit “panic buying” during the COVID-19 crisis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:transa:v:191:y:2025:i:c:s0965856424003653
    DOI: 10.1016/j.tra.2024.104317
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856424003653
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2024.104317?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. Xiaodan Pan & Martin Dresner & Benny Mantin & Jun A. Zhang, 2020. "Pre‐Hurricane Consumer Stockpiling and Post‐Hurricane Product Availability: Empirical Evidence from Natural Experiments," Production and Operations Management, Production and Operations Management Society, vol. 29(10), pages 2350-2380, October.
    2. José Holguín-Veras & Miguel Jaller & Felipe Aros-Vera & Johanna Amaya & Trilce Encarnación & Tricia Wachtendorf, 2016. "Disaster Response Logistics: Chief Findings of Fieldwork Research," International Series in Operations Research & Management Science, in: Christopher W. Zobel & Nezih Altay & Mark P. Haselkorn (ed.), Advances in Managing Humanitarian Operations, chapter 3, pages 33-57, Springer.
    3. Allen, Jaime & Bellizzi, Maria Grazia & Eboli, Laura & Forciniti, Carmen & Mazzulla, Gabriella, 2020. "Service quality in a mid-sized air terminal: A SEM-MIMIC ordinal probit accounting for travel, sociodemographic, and user-type heterogeneity," Journal of Air Transport Management, Elsevier, vol. 84(C).
    4. Richard J. Gilbert & Paul Klemperer, 2000. "An Equilibrium Theory of Rationing," RAND Journal of Economics, The RAND Corporation, vol. 31(1), pages 1-21, Spring.
    5. Holguín-Veras, José & Encarnación, Trilce & Van Wassenhove, Luk N. & Pokharel, Shaligram & Cantillo, Víctor & Amaya, Johanna & Wachtendorf, Tricia & Rilling, Juanita, 2022. "Reducing material convergence in disaster environments: The potential of trusted change agents," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
    6. Amaya, Johanna & Delgado-Lindeman, Maira & Arellana, Julian & Allen, Jaime, 2021. "Urban freight logistics: What do citizens perceive?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    7. Eisenman, D.P. & Cordasco, K.M. & Asch, S. & Golden, J.F. & Glik, D., 2007. "Disaster planning and risk communication with vulnerable communities: lessons from Hurricane Katrina," American Journal of Public Health, American Public Health Association, vol. 97(S1), pages 109-115.
    8. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    9. E. L. Quarantelli, 1988. "Disaster Crisis Management: A Summary Of Research Findings," Journal of Management Studies, Wiley Blackwell, vol. 25(4), pages 373-385, July.
    10. Kum Fai Yuen & Xueqin Wang & Fei Ma & Kevin X. Li, 2020. "The Psychological Causes of Panic Buying Following a Health Crisis," IJERPH, MDPI, vol. 17(10), pages 1-14, May.
    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. Yu Yuan & Bo Fan, 2024. "Protective consumption behavior under smog: using a data-driven dynamic Bayesian network," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(2), pages 4133-4151, February.
    2. Grace Chua & Kum Fai Yuen & Xueqin Wang & Yiik Diew Wong, 2021. "The Determinants of Panic Buying during COVID-19," IJERPH, MDPI, vol. 18(6), pages 1-28, March.
    3. Chen, Tinggui & Jin, Yumei & Yang, Jianjun & Cong, Guodong, 2022. "Identifying emergence process of group panic buying behavior under the COVID-19 pandemic," Journal of Retailing and Consumer Services, Elsevier, vol. 67(C).
    4. Yusen Ye & Wen Jiao & Hong Yan, 2020. "Managing Relief Inventories Responding to Natural Disasters: Gaps Between Practice and Literature," Production and Operations Management, Production and Operations Management Society, vol. 29(4), pages 807-832, April.
    5. Plat, Richard, 2009. "Stochastic portfolio specific mortality and the quantification of mortality basis risk," Insurance: Mathematics and Economics, Elsevier, vol. 45(1), pages 123-132, August.
    6. Kondylis, Athanassios & Whittaker, Joe, 2008. "Spectral preconditioning of Krylov spaces: Combining PLS and PC regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2588-2603, January.
    7. Ouyang, Yaofu & Li, Peng, 2018. "On the nexus of financial development, economic growth, and energy consumption in China: New perspective from a GMM panel VAR approach," Energy Economics, Elsevier, vol. 71(C), pages 238-252.
    8. Paschalis Arvanitidis & Athina Economou & Christos Kollias, 2016. "Terrorism’s effects on social capital in European countries," Public Choice, Springer, vol. 169(3), pages 231-250, December.
    9. Rizvi, Syed Kumail Abbas & Rahat, Birjees & Naqvi, Bushra & Umar, Muhammad, 2024. "Revolutionizing finance: The synergy of fintech, digital adoption, and innovation," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    10. Boone, J., 2003. "Optimal Competition : A Benchmark for Competition Policy," Discussion Paper 2003-3, Tilburg University, Center for Economic Research.
    11. Teerachai Amnuaylojaroen & Pavinee Chanvichit, 2024. "Historical Analysis of the Effects of Drought on Rice and Maize Yields in Southeast Asia," Resources, MDPI, vol. 13(3), pages 1-18, March.
    12. Paul, Ananna & Shukla, Nagesh & Trianni, Andrea, 2023. "Modelling supply chain sustainability challenges in the food processing sector amid the COVID-19 outbreak," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    13. Weili Duan & Bin He & Daniel Nover & Guishan Yang & Wen Chen & Huifang Meng & Shan Zou & Chuanming Liu, 2016. "Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods," Sustainability, MDPI, vol. 8(2), pages 1-15, January.
    14. Adele Ravagnani & Fabrizio Lillo & Paola Deriu & Piero Mazzarisi & Francesca Medda & Antonio Russo, 2024. "Dimensionality reduction techniques to support insider trading detection," Papers 2403.00707, arXiv.org, revised May 2024.
    15. Jenni Dinger & Michael Conger & David Hekman & Carla Bustamante, 2020. "Somebody That I Used to Know: The Immediate and Long-Term Effects of Social Identity in Post-disaster Business Communities," Journal of Business Ethics, Springer, vol. 166(1), pages 115-141, September.
    16. Cling, Jean-Pierre & Delecourt, Clément, 2022. "Interlinkages between the Sustainable Development Goals," World Development Perspectives, Elsevier, vol. 25(C).
    17. Hino, Hideitsu & Wakayama, Keigo & Murata, Noboru, 2013. "Entropy-based sliced inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 105-114.
    18. Angelucci, Federica & Conforti, Piero, 2010. "Risk management and finance along value chains of Small Island Developing States. Evidence from the Caribbean and the Pacific," Food Policy, Elsevier, vol. 35(6), pages 565-575, December.
    19. Lynch, Patrick & O'Toole, Thomas & Biemans, Wim, 2014. "From conflict to crisis in collaborative NPD," Journal of Business Research, Elsevier, vol. 67(6), pages 1145-1153.
    20. Poskitt, D.S. & Sengarapillai, Arivalzahan, 2013. "Description length and dimensionality reduction in functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 98-113.

    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:eee:transa:v:191:y:2025:i:c:s0965856424003653. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    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.