Journal article
Determinants of Business Resilience in the Restaurant Industry During the COVID-19 Pandemic: A Textual Analytics Study on an O2O Platform Case
IEEE transactions on engineering management, Vol.71, pp.10427-10440
2022
DOI: 10.1109/TEM.2022.3187986
Abstract
Understanding the resilience capabilities of restaurant operations and the determinants affecting these capabilities is critical to helping restaurants overcome the hardships owing to the coronavirus disease (COVID-19) pandemic. This article adopts a textual analytics approach to scientifically measure consumption trends and identify the shock to restaurant sales using online customer review data from Dianping.com (an O2O platform in China). Moreover, the article proposes a theoretical model of business resilience for the restaurant industry in the context of the pandemic. Then, an empirical investigation on how the determinants in our theoretical framework affect the resilience of restaurant business operations using the panel logit model is conducted. Our findings indicate that the pandemic has severely disrupted the full-service restaurants as compared to the quick-service restaurants. We identify four determinants of resilience, namely social capital (i.e., restaurant rating), physical capital (i.e., contactless service), economic capital (i.e., chain operation), and natural capital (e.g., location), which are significantly associated with the resilience of restaurant business during the pandemic. These four determinants play different roles in the resilience of full-service and quick-service restaurants. The findings of this study have theoretical contribution and generate some important managerial implications for helping the restaurant industry recover from disruptions brought by the COVID-19 pandemic.
Details
- Title: Subtitle
- Determinants of Business Resilience in the Restaurant Industry During the COVID-19 Pandemic: A Textual Analytics Study on an O2O Platform Case
- Creators
- Wei Liu - Dongbei University of Finance and EconomicsTsan-Ming Choi - University of LiverpoolXueqi Niu - Dongbei University of Finance and EconomicsMin Zhang - University of IowaWeiguo Fan - University of Iowa
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on engineering management, Vol.71, pp.10427-10440
- Publisher
- IEEE
- DOI
- 10.1109/TEM.2022.3187986
- ISSN
- 0018-9391
- eISSN
- 1558-0040
- Number of pages
- 14
- Grant note
- LJKR0443 / EducationDepartment of Liaoning Province 71831003 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) 18ZDA042 / National Social Science Foundation of China
- Language
- English
- Electronic publication date
- 2022
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380476302771
Metrics
56 Record Views