Dine in or Takeout? Trends on Restaurant Service Demand amid the COVID-19 Pandemic

References

• Agatz N, Cho S-H, Sun H, Wang H (2024) Transportation-enabled services: Concept, framework, and research opportunities. Service Sci. 16(1):1–21.Link, Google Scholar
• Ahuja K, Chandra V, Lord V, Peens C (2021) Ordering In: The Rapid Evolution of Food Delivery, vol. 22 (McKinsey & Company, New York), 1–13.Google Scholar
• Bacher J (2000) A probabilistic clustering model for variables of mixed type. Quality Quantity 34(3):223–235.Crossref, Google Scholar
• Bahrami S, Nourinejad M, Yin Y, Wang H (2023) The three-sided market of on-demand delivery. Transportation Res. Part E Logist. Transportation Rev. 179:103313.Crossref, Google Scholar
• Belarmino A, Raab C, Tang J, Han W (2021) Exploring the motivations to use online meal delivery platforms: Before and during quarantine. Internat. J. Hospitality Management 96:102983.Crossref, Google Scholar
• Bhatti A, Akram H, Basit HM, Khan AU, Mahwish S, Naqvi R, Bilal M (2020) E-commerce trends during COVID-19 pandemic. Internat. J. Future Generation Comm. Networking 13(2):1449–1452.Google Scholar
• Bock HH (1996) Probabilistic models in cluster analysis. Comput. Statist. Data Anal. 23(1):5–28.Crossref, Google Scholar
• Cao J, Menendez M (2012) Accuracy study of parking duration data from patrol survey. 12th Swiss Transport Res. Conf. (Ascona, Switzerland) .Google Scholar
• Chen Q, Conway A, Cheng J (2017) Parking for residential delivery in New York City: Regulations and behavior. Transport Policy 54:53–60.Crossref, Google Scholar
• Chen M, Hu M, Wang J (2022) Food delivery service and restaurant: Friend or foe? Management Sci. 68(9):6539–6551.Link, Google Scholar
• Chu J, Liu H, Salvo A (2021) Air pollution as a determinant of food delivery and related plastic waste. Nature Human Behav. 5(2):212–220.Crossref, Google Scholar
• Collison J (2020) The impact of online food delivery services on restaurant sales. Accessed August 11, 2021, https://web.stanford.edu/∼leinav/teaching/Collison.pdf.Google Scholar
• De Haas M, Faber R, Hamersma M (2020) How COVID-19 and the Dutch “intelligent lockdown” change activities, work and travel behaviour: Evidence from longitudinal data in the Netherlands. Transportation Res. Interdisciplinary Perspect. 6:100150.Crossref, Google Scholar
• Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. B 39(1):1–22.Crossref, Google Scholar
• Dias FF, Lavieri PS, Sharda S, Khoeini S, Bhat CR, Pendyala RM, Pinjari AR, Ramadurai G, Srinivasan KK (2020) A comparison of online and in-person activity engagement: The case of shopping and eating meals. Transportation Res. Part C Emerging Tech. 114:643–656.Crossref, Google Scholar
• Du J, Rakha HA (2020) COVID-19 Impact on Ride-Hailing: The Chicago Case Study (Findings).Google Scholar
• Feldman P, Frazelle AE, Swinney R (2023) Managing relationships between restaurants and food delivery platforms: Conflict, contracts, and coordination. Management Sci. 69(2):812–823.Link, Google Scholar
• Gao X, Shi X, Guo H, Liu Y (2020) To buy or not buy food online: The impact of the COVID-19 epidemic on the adoption of e-commerce in China. PLoS One 15(8):e0237900.Crossref, Google Scholar
• Gao S, Li M, Liang Y, Marks J, Kang Y, Li M (2019) Predicting the spatiotemporal legality of on-street parking using open data and machine learning. Ann. GIS 25(4):299–312.Crossref, Google Scholar
• Garrow L, Lurkin V (2021) How COVID-19 is impacting and reshaping the airline industry. J. Revenue Pricing Management 20(1):3–9.Crossref, Google Scholar
• Gkiotsalitis K, Cats O (2021) Public transport planning adaption under the COVID-19 pandemic crisis: Literature review of research needs and directions. Transportation Rev. 41(3):374–392.Crossref, Google Scholar
• Griesbach K, Reich A, Elliott-Negri L, Milkman R (2019) Algorithmic control in platform food delivery work. Socius 5:2378023119870041.Crossref, Google Scholar
• Griffith E, Eavis P (2020) DoorDash reveals I.P.O. filing. Accessed December 1, 2020, https://www.nytimes.com/2020/11/13/technology/doordash-reveals-ipo-filing.html.Google Scholar
• Haddad AJ, Mondal A, Bhat CR (2023) Eat-in or eat-out? A joint model to analyze the new landscape of dinner meal preferences. Transportation Res. Part C Emerging Tech. 147:104016.Crossref, Google Scholar
• Han BR, Sun T, Chu LY, Wu L (2022) COVID-19 and e-commerce operations: Evidence from Alibaba. Manufacturing Service Oper. Management 24(3):1388–1405s.Link, Google Scholar
• Henry-Nickie M, Hudak J (2020) Social Distancing in Black and White Neighborhoods in Detroit: A Data-Driven Look at Vulnerable Communities (Brookings, Washington, DC).Google Scholar
• Ilin C, Annan-Phan S, Tai XH, Mehra S, Hsiang S, Blumenstock JE (2021) Public mobility data enables COVID-19 forecasting and management at local and global scales. Sci. Rep. 11(1):1–11.Crossref, Google Scholar
• Jain AK, Dubes RC (1988) Algorithms for Clustering Data (Prentice-Hall, Inc., Hoboken, NJ).Google Scholar
• Jia H, Shen S, Ramírez García JA, Shi C (2022) Partner with a third-party delivery service or not? A prediction-and-decision tool for restaurants facing takeout demand surges during a pandemic. Service Sci. 14(2):139–155.Link, Google Scholar
• Juhász L, Hochmair HH (2020) Studying spatial and temporal visitation patterns of points of interest using SafeGraph data in Florida. Accessed August 11, 2021, https://digitalcommons.fiu.edu/gis/79/.Google Scholar
• Karniouchina K, Sarangee K, Theokary C, Kübler R (2022) The impact of the COVID-19 pandemic on restaurant resilience: Lessons, generalizations, and ideas for future research. Service Sci. 14(2):121–138.Link, Google Scholar
• Kawasaki T, Wakashima H, Shibasaki R (2022) The use of e-commerce and the COVID-19 outbreak: A panel data analysis in Japan. Transport Policy 115:88–100.Crossref, Google Scholar
• Lautso K (1981) Mathematical relationships among parking characteristics and revising and reduction methods of parking field survey information. Transportation Res. Part B Methodological 15(2):73–83.Crossref, Google Scholar
• Li Z, Wang G (2020) The role of on-demand delivery platforms in restaurants. Preprint, submitted March 29, 2021, https://dx.doi.org/10.2139/ssrn.3813891.Google Scholar
• Liu Y, Li S (2023) An economic analysis of on-demand food delivery platforms: Impacts of regulations and integration with ride-sourcing platforms. Transportation Res. Part E Logist. Transportation Rev. 171:103019.Crossref, Google Scholar
• Loa P, Hossain S, Liu Y, Habib KN (2021) How have ride-sourcing users adapted to the first wave of the COVID-19 pandemic? Evidence from a survey-based study of the greater Toronto area. Transportation Lett. 13(5–6):404–413.Crossref, Google Scholar
• Loa P, Hossain S, Liu Y, Habib KN (2022) How has the COVID-19 pandemic affected the use of ride-sourcing services? An empirical evidence-based investigation for the greater Toronto area. Transportation Res. Part A Policy Practice 155:46–62.Crossref, Google Scholar
• Ma Y, Pei S, Shaman J, Dubrow R, Chen K (2021) Role of meteorological factors in the transmission of SARS-CoV-2 in the United States. Nature Comm. 12(1):1–9.Google Scholar
• McLachlan G, Jones P (1988) Fitting mixture models to grouped and truncated data via the EM algorithm. Biometrics 44(2):571–578.Crossref, Google Scholar
• Morgan Stanley (2020) COVID-19 era serves up big changes for U.S. restaurants. Accessed August 11, 2021, https://www.morganstanley.com/ideas/coronavirus-restaurant-trends.Google Scholar
• Morshed SA, Khan SS, Tanvir RB, Nur S (2021) Impact of COVID-19 pandemic on ride-hailing services based on large-scale Twitter data analysis. J. Urban Management 10(2):155–165.Crossref, Google Scholar
• Muller C (2018) Restaurant delivery: Are the “ODP” the industry’s “OTA”? Part II. Boston Hospitality Rev. 6(3):1–17.Google Scholar
• Norris CL, Taylor S Jr, Taylor DC (2021) Pivot! How the restaurant industry adapted during COVID-19 restrictions. Internat. Hospitality Rev. 35(2).Crossref, Google Scholar
• Oblander ES, McCarthy DM (2021) How has COVID-19 impacted customer relationship dynamics at restaurant food delivery businesses. Marketing Sci. Inst. Working Paper Ser. 23:2139.Google Scholar
• Oblander S, McCarthy DM (2023) Frontiers: Estimating the long-term impact of major events on consumption patterns: Evidence from COVID-19. Marketing Sci. 42(5):839–852.Link, Google Scholar
• Painter M, Qiu T (2021) Political beliefs affect compliance with government mandates. J. Econom. Behav. Organ. 185:688–701.Crossref, Google Scholar
• Pearson K (1894) Contributions to the mathematical theory of evolution. Philos. Trans. Roy. Soc. London A 185:71–110.Crossref, Google Scholar
• Pérez BO, Ferrin RS, Lipscomb D, Ford A, MacNeil L, Heider J, Hanson P (2021) Dynamic curbside management in the age of new mobility and e-commerce: Case studies from Columbus, OH and Washington, DC. Technical report, Washington, DC.Google Scholar
• Prestby T, App J, Kang Y, Gao S (2020) Understanding neighborhood isolation through spatial interaction network analysis using location big data. Environ. Planning A Econom. Space 52(6):1027–1031.Crossref, Google Scholar
• Raj M, Sundararajan A, You C (2020) COVID-19 and digital resilience: Evidence from Uber Eats. Accessed December 1, 2020, https://arxiv.org/abs/2006.07204.Google Scholar
• Shamshiripour A, Rahimi E, Shabanpour R, Mohammadian AK (2020) How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago. Transportation Res. Interdisciplinary Perspect. 7:100216.Crossref, Google Scholar
• Silveira-Santos T, González ABR, Rangel T, Pozo RF, Vassallo JM, Díaz JJV (2022) Were ride-hailing fares affected by the COVID-19 pandemic? Empirical analyses in Atlanta and Boston. Transportation 1–32.Google Scholar
• Song G, Zhang H, Duan H, Xu M (2018) Packaging waste from food delivery in China’s mega cities. Resources Conservation Recycling 130:226–227.Crossref, Google Scholar
• Teimouri M (2021) EM algorithm for mixture of skew-normal distributions fitted to grouped data. J. Appl. Statist. 48(7):1154–1179.Crossref, Google Scholar
• Teixeira JF, Lopes M (2020) The link between bike sharing and subway use during the COVID-19 pandemic: The case-study of New York’s Citi Bike. Transportation Res. Interdisciplinary Perspect. 6:100166.Crossref, Google Scholar
• TSYS (2018) U.S. consumer payment study. Accessed December 1, 2020, https://www.tsys.com/Assets/TSYS/downloads/rs_2018-us-consumer-payment-study.pdf.Google Scholar
• Yang S, Qian ZS (2017) Turning meter transactions data into occupancy and payment behavioral information for on-street parking. Transportation Res. Part C Emerging Tech. 78:165–182.Crossref, Google Scholar
• Yeo L (2020) Which company is winning the restaurant food delivery war? Accessed December 1, 2020, https://secondmeasure.com/datapoints/food-delivery-services-grubhub-uber-eats-doordash-postmates/.Google Scholar
• Yu J, Xie N, Zhu J, Qian Y, Zheng S, Chen XM (2022) Exploring impacts of COVID-19 on city-wide taxi and ride-sourcing markets: Evidence from Ningbo, China. Transport Policy 115:220–238.Crossref, Google Scholar
• Zhang F, Ji Y, Lv H, Ma X, Kuai C, Li W (2022) Investigating factors influencing takeout shopping demand under COVID-19: Generalized additive mixed models. Transportation Res. Part D Transportation Environ. 107:103285.Crossref, Google Scholar
• Zheng H, Zhang K, Nie YM (2021) Plunge and rebound of a taxi market through COVID-19 lockdown: Lessons learned from Shenzhen, China. Transportation Res. Part A Policy Practice 150:349–366.Crossref, Google Scholar