Examining factors influencing the user’s loyalty on algorithmic news recommendation service

Abstract

Based on user-related data, an algorithmic news recommendation service (ANRS) predicts users’ reading preferences and selectively recommends news. Given the double-edged opinions on ANRS, identifying and managing crucial factors influencing users’ satisfaction and trust in this service will be essential for service providers and developers. However, few studies have tried to find these factors or develop a more precise understanding of users’ perceptions of this service. Therefore, this study aims to examine factors affecting users’ loyalty to ANRS with partial least squares structural equation modelling (PLS-SEM). This study conducted an online survey for users of “My News”, the free mobile ANRS of NAVER, Korea’s dominant online portal site, and analyzed the data from 483 responses. This analysis verified that both satisfaction and trust positively affect loyalty to ANRS, and trust positively affects satisfaction. Moreover, it was found that perceived accuracy positively affects satisfaction. The result also showed that perceived news value and perceived transparency positively affect trust, and privacy concerns negatively affect it. Lastly, it was found that perceived usability and pre-existing attitude toward the service provider positively affect satisfaction and trust. The results and discussions will be helpful for service providers and developers to manage ANRS effectively based on users’ responses and perceptions of this service.