AI-generated reviews have become a core tool for hotel booking platforms in assisting user decision-making, yet existing research has not systematically explored the emotional tendencies of GAI reviews or the potential mechanisms by which temporal disclosure influences user decisions. This study integrates the Information Adoption Model (IAM) and the Elaboration Likelihood Model (ELM), incorporates the affordance perspective of GAI, employs a 2 × 2 experimental design (temporal disclosure × review valence), analyzes sample data from 649 travelers, and conducts empirical analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results demonstrate that temporal disclosure significantly enhances cognitive involvement; the effect of review valence on cognitive involvement is moderated by GAI affordance—high GAI affordance mitigates the inhibitory impact of positive reviews on cognitive involvement; empathy toward GAI information does not directly reduce psychological distance to hotels but operates indirectly through full mediation via psychological distance to GAI; psychological distance to GAI also indirectly influences booking intent via full mediation through psychological distance to the hotel. This study elucidates the mechanisms linking the emotional characteristics of AI-generated reviews with the impact of temporal disclosure on traveler decision-making, supplements theoretical frameworks for consumer decision-making in AI contexts, expands the IAM for AI applications, and provides empirical evidence and practical recommendations for optimizing GAI review presentation strategies on booking platforms.