2022 Volume 73 Issue 1 Pages 15-26
The purpose of this research is to clarify the relationship between word-of-mouth content and hotel rankings in a Japanese travel information website. We conducted a latent semantic analysis of word-of-mouth data based on the Latent Dirichlet Allocation (LDA) model and created variables for word-of-mouth content (topic indexes). A hierarchical multiple regression analysis was performed on the ranking order of the objective variable, with the control variable (customer and hotel characteristics) as the explanatory variables in the first stage and the topic index as the explanatory variable in the second stage.
We used 3,769 word-of-mouth data written for 79 hotels between October 1, 2019 and September 30, 2020 for the analysis. As a result, five topics (bath and hot springs, cleanness, virus protection, staff, and meals with family) were extracted from the word-of-mouth data and five topic indexes were created. We found a statistically significant positive impact of the "virus protection," “staff” and “meals with family” topic indexes on hotel rankings. It is important for hotel operators to provide personalized and attentive services to each guest, to improve its ranking and for guests to enjoy special moments with their family and friends on a vacation under a secure environment.
An analytical model for clarifying the relationship between word-of-mouth content (unstructured data) and ranking order (structured data) is presented, and can be practically applied to show hotel operators how to improve their hotel services to increase their ranking on travel information websites.
