Honor the Contract? Effects of Algorithmic Recommendation System Features on Perceived Benefits, Privacy Risk, and Continuance Intention to Use TikTok

Abstract

Algorithmic recommendations, characterized by features including accuracy, familiarity, novelty, and transparency, have been used in TikTok for a long time. However, the impact of these features on users’ privacy calculus and continuance usage intention remains unclear. Survey data from 625 Chinese users was analyzed using the least-squares partial structural equation modelling. Results showed that four features positively affect users’ perceived benefits; perceived familiarity positively affects privacy risk; perceived effectiveness of privacy policy moderates the relationship between perceived accuracy and privacy risk. Moreover, perceived benefits positively influence continuance intention to use, whereas privacy risk has a negative impact. This study shed light on why users are willing to compromise privacy for the benefits originating from features of algorithmic recommendations. Furthermore, this study provided valuable insights for platforms and practitioners concerning the design and development of effective algorithmic recommendations, as well as improving the effectiveness of privacy policy.