The rise of peer-to-peer platforms leads customers to base their opinion
on electronic word of mouth more than before, which necessitates the
update of factors affecting check-in behavior of customers. Though many
industries have been part of this tendency, hospitality industry comes in
sight most especially. In this article, the author defines the strongest
influences affecting consumer decision-making process when opting for
a restaurant. By processing Yelp open-source dataset and applying 9
machine learning models, it becomes clear that Random Forest model is
the best tool to predict check-in behavior of diners. On top of that, the
number of photos, number of friends are the best electronic factors, while
trendiness, kid-friendliness and timing are the most contributing
traditional aspects.
Citation:
Huseynova, F. (2022). Restaurateur's dilemma: Solved. ASERC Journal of Socio-Economic Studies, 5(1), 17-33. DOI:10.30546/2663-7251.2022.5.1.17