Proposing a Bayesian hierarchical growth curve model (BHGCM) for tourism and hospitality research

Abstract

Curvilinear growth trajectories of products/services are common in the tourism and hospitality industry. To fit nonlinear growth patterns with multilevel structures, this study proposed Bayesian hierarchical growth curve models (BHGCMs) in line with the increasing adoption of Bayesian analysis in tourism and hospitality academia. We provided the basic form of BHGCM along with its unique advantages. For an empirical test, this study applied several growth curves to approximate online reviews of U.S. hotels from August 2020 to January 2021. After selecting a Gompertz curve as a mean function of the GCM, a Bayesian hierarchical approach was employed to estimate growth parameters—namely, base and maximum volume of hotel reviews, inflection week, and relative growth rate—and identify their determinants. Our findings demonstrate the superiority of the proposed BHGCM in fitting the growth patterns of hotel reviews while revealing the effect of price and accumulated reviews on the parameters.