Exploring Foodborne Illness and Restaurant Cleanliness Reporting in Customer-Generated Online Reviews Using Business Analytics
Foodborne illness cases are chronically underreported, and it is crucial to investigate nontraditional strategies and approaches to identify food safety challenges that could lead to outbreaks. This point is especially important in the context of the food service industry because 61% of all foodborne illness outbreaks are attributed to restaurants. The overarching goal of our study was to data mine customer-generated restaurant reviews on an online review website and analyze the frequency at which restaurant patrons report specific terms related to foodborne illness and restaurant cleanliness. Our data analysis indicated statistically significant inverse correlations between the increased frequency of keywords in online reviews and customer satisfaction. The results from our study can be used to incentivize restaurateurs to implement enhanced food practices. Furthermore, the text mining methodology can be used in future studies to monitor food safety reporting in global markets.
Speaker / Author:
Jack R. Hodges, Conrad N. Hilton College of Global Hospitality Leadership, University of Houston
Minwoo Lee, PhD, Conrad N. Hilton College of Global Hospitality Leadership, University of Houston
Agnes DeFranco, PhD, Conrad N. Hilton College of Global Hospitality Leadership, University of Houston
Sujata A. Sirsat, PhD, Conrad N. Hilton College of Global Hospitality Leadership, University of Houston