Unveiling the Drivers of Food Delivery App Continuance Intention: Integrating Expectation Confirmation Model and Task-Technology Fit Model

This study aims to predict the factors influencing the continuance intention of food delivery apps through an integrated expectation-confirmation model and task-technology fit. The study collected data from 353 users of food delivery apps and analyzed it using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results of the study show that task-technology fit significantly influences the confirmation of expectations and perceived usefulness of food delivery apps, which in turn positively influence satisfaction and continuance intention to use food delivery apps. The mediating effects of satisfaction on the relationships between confirmation of expectations and continuance intention, as well as between perceived usefulness and continuance intention, were also significant. The study’s findings confirmed the moderating effect of personal innovativeness on the relationship between task-technology fit and confirmation of expectations. Furthermore, a multigroup analysis shows no significant differences between age and gender groups. This study contributed to the expectation-confirmation model by integrating task-technology fit and personal innovativeness as crucial constructs that significantly influence the food delivery apps’ post-adoption. The findings of the current study provide useful guidance for restaurant chain managers in designing innovative apps that offer a seamless customer experience and encourage customers to place orders through food delivery apps.