레스토랑 예약 앱 사용자 리뷰의 시계열적 감성 및 주제 반응 탐색: 정교화 가능성 모델 관점에서의 실증 분석

With the proliferation of digital platforms, consumer reviews have come to play a pivotal role in shaping service quality and brand trust, reflecting users’ experiences, emotions, and perceptions of functional stability. In particular, restaurant reservation apps are of significant academic and practical value, as online reviews are directly linked to offline consumer behaviors. However, previous studies have predominantly focused on structural information such as star ratings or review length, or have been limited to analyses at specific points in time, thus failing to sufficiently explain the temporal evolution and affective dynamics of review messages. To address this gap, the present study adopts the Elaboration Likelihood Model (ELM) to define functionality-oriented messages as central route and affective expressions as peripheral route, and dynamically analyzes their temporal structural changes. Integrating LDA topic modeling, sentiment analysis, and co-occurrence network analysis, we examine 46,392 OpenTable app reviews collected from 2009 to 2023. We analyze the correlations among sentiment scores, review length, and review frequency, and compare changes across three periods: early, middle, and late stages. The results show that central route reviews are primarily composed of functional and informational evaluations, whereas peripheral route reviews are characterized by positive affective content. Notably, in the late period, functional dissatisfaction tends to spread as negative sentiment through the peripheral route, suggesting a new potential role for affective pathways that was overlooked in prior theories. By conducting a time-series analysis of user reviews, this study empirically demonstrates the structural evolution of persuasive messages and offers meaningful insights for application strategy development and practical decision-making.