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

大流行 2019年冠状病毒病(COVID-19) 业务 2019-20冠状病毒爆发 服务(商务) 严重急性呼吸综合征冠状病毒2型(SARS-CoV-2) 营销 病毒学 医学 爆发 疾病 病理 传染病(医学专业)
作者
Linxuan Shi,Zhengtian Xu
出处
期刊:Service science [Institute for Operations Research and the Management Sciences]
卷期号:16 (4): 241-271 被引量:3
标识
DOI:10.1287/serv.2023.0103
摘要

The COVID-19 pandemic has caused unprecedented damage to restaurant businesses, especially indoor dining services, because of the widespread fear of coronavirus exposure. In contrast, the online food ordering and delivery services, led by DoorDash, Grubhub, and Uber Eats, filled in the vacancy and achieved explosive growth. As a result, the restaurant industry is experiencing dramatic transformations under the crossfire of these two driving forces. However, these changes are not fully exposed because of the lack of firsthand data, let alone their potential consequences and implications. This study, thus, leverages foot traffic data to reveal and understand the trends of restaurant service demand through the pandemic. We devise a mixture model to decompose the aggregate foot traffic by dwelling time patterns into dine-in and takeout volumes. The transitions of demand structures are then identified for various restaurant sectors by service types, price levels, and locations. We observe that limited-service and budget restaurants saw a significantly faster recovery than full-service counterparts given their comparative advantages in adapting toward takeout channels. But, in the long run, our results suggest more robust demands for dine-in services at full-service restaurants, particularly those that provide more premium dining experiences. Comparatively, the off-line channels at limited-service restaurants appeared vulnerable to the cannibalization from online ordering and delivery channels, which strengthened even after society moved out of lockdown. Regionally, exurban restaurants seem to trend toward the takeout mode, whereas urban areas did not see a notable modal migration between dine-in and takeout from restaurants.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
Haoxiang发布了新的文献求助10
2秒前
科研通AI5应助叶成帷采纳,获得10
3秒前
大个应助禾火采纳,获得10
3秒前
万能图书馆应助c-zhang采纳,获得10
4秒前
雪梨101完成签到,获得积分10
4秒前
郭辉发布了新的文献求助10
4秒前
方1111完成签到,获得积分20
4秒前
华仔应助Regina采纳,获得10
5秒前
5秒前
帅气的璎发布了新的文献求助10
7秒前
Ava应助wangwenzhe采纳,获得10
7秒前
NexusExplorer应助方1111采纳,获得10
8秒前
8秒前
9秒前
科研小白完成签到,获得积分10
10秒前
11秒前
Singularity完成签到,获得积分0
12秒前
无限飞丹发布了新的文献求助10
12秒前
SciGPT应助桑葚啊采纳,获得10
12秒前
13秒前
李大宝发布了新的文献求助10
13秒前
14秒前
c-zhang发布了新的文献求助10
16秒前
禾火发布了新的文献求助10
16秒前
yatou327完成签到,获得积分10
17秒前
XL发布了新的文献求助10
18秒前
喵总完成签到,获得积分10
20秒前
21秒前
21秒前
c-zhang完成签到,获得积分20
22秒前
叶成帷完成签到,获得积分10
24秒前
YXH发布了新的文献求助10
27秒前
Kathy发布了新的文献求助30
28秒前
31秒前
dodo完成签到,获得积分0
31秒前
小魏完成签到,获得积分10
32秒前
32秒前
YXH完成签到,获得积分10
33秒前
无限飞丹完成签到,获得积分10
35秒前
35秒前
高分求助中
【此为提示信息,请勿应助】请按要求发布求助,避免被关 20000
Les Mantodea de Guyane Insecta, Polyneoptera 2500
Computational Atomic Physics for Kilonova Ejecta and Astrophysical Plasmas 500
Technologies supporting mass customization of apparel: A pilot project 450
Cybersecurity Blueprint – Transitioning to Tech 400
Mixing the elements of mass customisation 400
Периодизация спортивной тренировки. Общая теория и её практическое применение 310
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3782342
求助须知:如何正确求助?哪些是违规求助? 3327852
关于积分的说明 10233274
捐赠科研通 3042733
什么是DOI,文献DOI怎么找? 1670153
邀请新用户注册赠送积分活动 799658
科研通“疑难数据库(出版商)”最低求助积分说明 758876