Algorithm Awareness and User Motivation as Predictors of TikTok Engagement Among Generation Z in South Jakarta

This study investigates the relationship between motivation, algorithmic awareness, and engagement behavior among Generation Z TikTok users in South Jakarta, Indonesia. Guided by the uses and gratifications theory, we examine how user motivation drives engagement on an algorithmically curated platform and whether algorithmic awareness directly influences or moderates this relationship. Data from 423 respondents collected through an online survey using purposive sampling were analyzed using partial least squares structural equation modeling (PLS-SEM). Results indicate that motivation significantly enhances engagement behavior, especially through entertainment, self-expression, and social interaction gratification. Algorithmic awareness, however, does not directly predict engagement but significantly moderates the motivation and engagement link, thereby weakening it. This suggests that users with higher awareness engage more critically and selectively, reflecting algorithmic aversion tendencies where awareness prompts reflective rather than impulsive participation. The findings extend UGT into algorithm-driven environments, positioning algorithmic awareness as a cognitive boundary condition in digital behavior. The study contributes both theoretically and practically by highlighting the importance of algorithmic literacy in understanding engagement dynamics and guiding platform strategies that foster informed and intentional use. Future research should test this framework across broader contexts and integrate psychological or social factors to explain the unexplained variance in engagement.