Leveraging data analytics to understand the relationship between restaurants' safety violations and COVID-19 transmission

Arthur Huang; Efrén de la Mora Velasco; Ashkan Farhangi; Anil Bilgihan; Melissa Farboudi Jahromi

doi:10.1016/j.ijhm.2022.103241

Leveraging data analytics to understand the relationship between restaurants' safety violations and COVID-19 transmission

Int J Hosp Manag. 2022 Jul:104:103241. doi: 10.1016/j.ijhm.2022.103241. Epub 2022 May 11.

Authors

Arthur Huang¹, Efrén de la Mora Velasco¹, Ashkan Farhangi², Anil Bilgihan³, Melissa Farboudi Jahromi¹

Affiliations

¹ Rosen College of Hospitality Management, University of Central Florida, Orlando, FL 32819, USA.
² Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL 32816, USA.
³ College of Business, Florida Atlantic University, Boca Raton, FL 33431, USA.

Abstract

This paper leverages natural language processing, spatial analysis, and statistical analysis to examine the relationship between restaurants' safety violations and COVID-19 cases. We used location-based consumers' complaints data during the early stage of business reopening in Florida, USA. First, statistical analysis was conducted to examine the correlation between restaurants' safety violations and COVID-19 transmission. Second, a neural network-based deep learning model was developed to perform topic modeling based on consumers' complaints. Third, spatial modeling of the complaints' geographic distributions was performed to identify the hotspots of consumers' complaints and COVID-19 cases. The results reveal a positive relationship between consumers' complaints about restaurants' safety violations and COVID-19 cases. In particular, consumers' complaints about personal protection measures had the highest correlation with COVID-19 cases, followed by environmental safety measures. Our analytical methods and findings shed light on customers' behavioral shifts and hospitality businesses' adaptive practices during a pandemic.

Keywords: COVID-19; Complaints; Neural networks; Restaurant; Safety violation; Spatial analysis.