Proceedings of the 2020 International Conference on Social Sciences and Big Data Application (ICSSBDA 2020)

Transformation and Sustainable Development of Traditional Catering Industry After COVID-19

Authors
Guihua Guo, Ying Hu, Yan Fang
Corresponding Author
Guihua Guo
Available Online 2 November 2020.
DOI
10.2991/assehr.k.201030.052How to use a DOI?
Keywords
catering industry, COVID-19, opportunity
Abstract

Under the impact of the epidemic, a large number of catering enterprises are faced with many problems. Such as sharp decline in operating income, rising costs and poor industry performance. However, crises and opportunities are always coexisted. This article analyzes the problems faced by the catering industry and explores opportunities after the epidemic by combining the response measures taken by outstanding companies. It is proposed to comprehensively expand online services, diversify sales methods and tap on its own unique characteristics, with the aim to provide some references and enlightenments for catering enterprises to seek survival in difficult situations.

Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2020 International Conference on Social Sciences and Big Data Application (ICSSBDA 2020)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
2 November 2020
ISBN
978-94-6239-266-3
ISSN
2352-5398
DOI
10.2991/assehr.k.201030.052How to use a DOI?
Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Guihua Guo
AU  - Ying Hu
AU  - Yan Fang
PY  - 2020
DA  - 2020/11/02
TI  - Transformation and Sustainable Development of Traditional Catering Industry After COVID-19
BT  - Proceedings of the 2020 International Conference on Social Sciences and Big Data Application (ICSSBDA 2020)
PB  - Atlantis Press
SP  - 254
EP  - 257
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.201030.052
DO  - 10.2991/assehr.k.201030.052
ID  - Guo2020
ER  -