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Predicting mobility using limited data during early stages of a pandemic

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  • Lash, Michael T.
  • Sajeesh, S.
  • Araz, Ozgur M.

Abstract

The COVID-19 pandemic has changed consumer behavior substantially. In this study, we explore the drivers of consumer mobility in several metropolitan areas in the United States under the perceived risks of COVID-19. We capture multiple dimensions of perceived risk using local and national cases and death counts of COVID-19, along with real-time Google Trends data for personal protective equipment (PPE). While Google Trends data are popular inputs in many studies, the risk of multicollinearity escalates with the addition of more relevant terms. Therefore, multicollinearity-alleviating methods are needed to appropriately leverage information provided by Google Trends data. We develop and utilize a novel optimization scheme to induce linear models containing strictly significant covariates and minimal multicollinearity. We find that there are a variety of unique factors that drive mobility in different geographic locations, as well as several factors that are common to all locations.

Suggested Citation

  • Lash, Michael T. & Sajeesh, S. & Araz, Ozgur M., 2023. "Predicting mobility using limited data during early stages of a pandemic," Journal of Business Research, Elsevier, vol. 157(C).
    • Handle: RePEc:eee:jbrese:v:157:y:2023:i:c:s0148296322008785
    DOI: 10.1016/j.jbusres.2022.113413
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    as
    1. Keane, Michael & Neal, Timothy, 2021. "Consumer panic in the COVID-19 pandemic," Journal of Econometrics, Elsevier, vol. 220(1), pages 86-105.
    2. Basu, Rounaq & Ferreira, Joseph, 2021. "Sustainable mobility in auto-dominated Metro Boston: Challenges and opportunities post-COVID-19," Transport Policy, Elsevier, vol. 103(C), pages 197-210.
    3. 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.
    4. Sharma, Amalesh & Adhikary, Anirban & Borah, Sourav Bikash, 2020. "Covid-19′s impact on supply chain decisions: Strategic insights from NASDAQ 100 firms using Twitter data," Journal of Business Research, Elsevier, vol. 117(C), pages 443-449.
    5. Bradlow, Eric T. & Gangwar, Manish & Kopalle, Praveen & Voleti, Sudhir, 2017. "The Role of Big Data and Predictive Analytics in Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 79-95.
    6. Stefan Henningsson & William J. Kettinger & Chen Zhang & Nageswaran Vaidyanathan, 2021. "Transformative rare events: Leveraging digital affordance actualisation," European Journal of Information Systems, Taylor & Francis Journals, vol. 30(2), pages 137-156, March.
    7. Dimitris Bertsimas & Dan A. Iancu & Dmitriy Katz, 2013. "A New Local Search Algorithm for Binary Optimization," INFORMS Journal on Computing, INFORMS, vol. 25(2), pages 208-221, May.
    8. Ma, Shaohui & Fildes, Robert, 2021. "Retail sales forecasting with meta-learning," European Journal of Operational Research, Elsevier, vol. 288(1), pages 111-128.
    9. Chernozhukov, Victor & Kasahara, Hiroyuki & Schrimpf, Paul, 2021. "Causal impact of masks, policies, behavior on early covid-19 pandemic in the U.S," Journal of Econometrics, Elsevier, vol. 220(1), pages 23-62.
    10. Simionescu, Mihaela & Raišienė, Agota Giedrė, 2021. "A bridge between sentiment indicators: What does Google Trends tell us about COVID-19 pandemic and employment expectations in the EU new member states?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    11. Diane E. Vaughan & Sheldon H. Jacobson & Shane N. Hall & Laura A. McLay, 2005. "Simultaneous Generalized Hill-Climbing Algorithms for Addressing Sets of Discrete Optimization Problems," INFORMS Journal on Computing, INFORMS, vol. 17(4), pages 438-450, November.
    12. Grechanovsky, Eugene & Pinsker, Ilia, 1995. "Conditional p-values for the F-statistic in a forward selection procedure," Computational Statistics & Data Analysis, Elsevier, vol. 20(3), pages 239-263, September.
    13. Chennamaneni, Pavan Rao & Echambadi, Raj & Hess, James D. & Syam, Niladri, 2016. "Diagnosing harmful collinearity in moderated regressions: A roadmap," International Journal of Research in Marketing, Elsevier, vol. 33(1), pages 172-182.
    14. Yutaka Kano & Akira Harada, 2000. "Stepwise variable selection in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 65(1), pages 7-22, March.
    15. Tonya Boone & Ram Ganeshan & Robert L. Hicks & Nada R. Sanders, 2018. "Can Google Trends Improve Your Sales Forecast?," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1770-1774, October.
    16. Benjamin Fritzsch & Kai Wenger & Philipp Sibbertsen & Georg Ullmann, 2020. "Can google trends improve sales forecasts on a product level?," Applied Economics Letters, Taylor & Francis Journals, vol. 27(17), pages 1409-1414, October.
    17. Massimiliano Caramia & Paolo Dell'Olmo & Giuseppe F. Italiano, 2008. "Novel Local-Search-Based Approaches to University Examination Timetabling," INFORMS Journal on Computing, INFORMS, vol. 20(1), pages 86-99, February.
    18. Anuj Kumar & Amit Mehra & Subodha Kumar, 2019. "Why Do Stores Drive Online Sales? Evidence of Underlying Mechanisms from a Multichannel Retailer," Service Science, INFORMS, vol. 30(1), pages 319-338, March.
    19. Pantano, Eleonora & Pizzi, Gabriele & Scarpi, Daniele & Dennis, Charles, 2020. "Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak," Journal of Business Research, Elsevier, vol. 116(C), pages 209-213.
    20. Joel Persson & Jurriaan F. Parie & Stefan Feuerriegel, 2021. "Monitoring the COVID-19 epidemic with nationwide telecommunication data," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(26), pages 2100664118-, June.
    21. Rivera, Roberto, 2016. "A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data," Tourism Management, Elsevier, vol. 57(C), pages 12-20.
    22. France, Stephen L. & Shi, Yuying & Kazandjian, Brett, 2021. "Web Trends: A valuable tool for business research," Journal of Business Research, Elsevier, vol. 132(C), pages 666-679.
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