Attributes of virtual and augmented reality tourism mobile applications predicting tourist behavioral engagement

Abstract

The study aims to explore the attributes of VR/AR tourism mobile applications (apps) and their impact on Tourist Behavioral Engagement (TBE), mediated by app satisfaction. The authors employed a text mining and Partial Least Squares-Structural Equation Modelling (PLS-SEM) approach to analyze 6,998 Google Play VR/AR tourism mobile apps online reviews. A word frequency matrix (WFM) was generated and used as an input for PLS-SEM to test the raised hypotheses. The findings revealed that utilitarian gratification, perceived usefulness, ease-of-use, and VR/AR immersion features (context awareness, presence, and telepresence) are key predictors of TBE, with app satisfaction playing a critical mediating role between these VR/AR attributions and TBE, emphasizing the vital role of Technology Acceptance Model (TAM) in driving sustained app engagement. The proposed framework advances theoretical literature on the TBE predictors and provides a guide for VR/AR tourism apps developers/designers to enable adoption, engagement, and proliferation among tourists.