Explaining Intention to Use Claude AI Among Indonesian Gen Z Students Through TAM, UTAUT2, and Algorithmic Trust

Abstrak

The rapid diffusion of large language model–based assistants (e.g., Claude AI) in higher education together with the accelerating adoption of AI among institutions and Gen Z learners creates an urgent need to understand what drives students to accept and intend to use these tools. This study examines how TAM (perceived usefulness, perceived ease of use), UTAUT2 (social influence, facilitating conditions, hedonic motivation, habit), and algorithmic trust explain attitudes and intention to use Claude AI among Indonesian Gen Z students. A cross-sectional quantitative survey was administered to 96 purposively selected Gen Z students at Makassar State University using Likert-scale measures, and the hypothesized relationships were tested using PLS-SEM with bootstrapping. Attitude toward using Claude AI (ATU) was the strongest predictor of behavioral intention (BI) (ATU → BI = 0.711; p < 0.001). ATU was primarily shaped by hedonic motivation and habit (both significant), with perceived usefulness contributing positively, while perceived ease of use and social influence were not significant. Facilitating conditions showed a significant negative effect on ATU and also reduced BI indirectly through ATU (FC → ATU = −0.175; FC → ATU → BI = −0.125). These findings imply that universities should prioritize enjoyable, habit-forming AI use and strengthen algorithm-relevant support (quality, training, and policy/ethical clarity), because poorly aligned “supporting conditions” may backfire and undermine students’ attitudes and intentions to adopt Claude AI.

Identifier

• DOI: https://doi.org/10.59562/0vcays50
• OpenAlex: https://openalex.org/W7169524421