Modeling the Impact of Online Review, Rating, and Tracking Systems on Continuance Intention in Food Delivery Platforms: A PLS-SEM Approach

The widespread adoption of online food delivery platforms has reshaped consumer decision-making through digital features such as online reviews, rating mechanisms, and real-time order tracking systems. Despite extensive research on online purchasing behavior, limited studies have jointly examined how these platform features influence purchase decisions and continuance intention from a data-driven decision-support perspective. This study analyzes the effects of online customer reviews, online customer ratings, and online customer tracking systems on continuance intention, with purchase decision serving as a mediating variable. Data were collected from 210 active users of the GoFood platform in Jakarta using a structured questionnaire. The proposed model was evaluated using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that online customer reviews and tracking systems have positive and significant effects on purchase decisions, while online customer ratings show a significant negative influence, indicating possible rating saturation or consumer skepticism. Purchase decision exhibits a strong positive effect on continuance intention and significantly mediates the relationships between digital platform features and sustained usage behavior. These findings highlight the role of platform-level system features as decision-support mechanisms that shape consumer behavior in online food delivery services. The study contributes to the literature on technology-enabled consumer decision processes and provides practical insights for platform developers to optimize system design to enhance user retention