Work place: Department Computer Science, Institute Teknologi Garut, Garut, 44151, Indonesia
E-mail: 2107014@itg.ac.id
Website:
Research Interests: Artificial Intelligence
Biography
Wiki Asri Iswandi was born in Garut, Indonesia, on February 28, 2003. She received the S.Kom. degree in information systems from Institute Technology Garut, Garut, Indonesia, in 2025.
She is currently a Research Assistant at Institute Technology Garut, Garut, Indonesia. Her current research interests include artificial intelligence (AI), system modelling, and information systems.
By Eri Satria Muhammad Rikza Nashrulloh Wiki Asri Iswandi
DOI: https://doi.org/10.5815/ijitcs.2026.03.05, Pub. Date: 8 Jun. 2026
The rapid integration of conversational Artificial Intelligence (AI) into instant messaging platforms has transformed how younger generations interact with digital technology. This study investigates Generation Z's engagement with Meta AI on WhatsApp by employing a modified Technology Acceptance Model (TAM) that partitions End-User Computing Satisfaction (EUCS) dimensions to ensure measurement validity. Specifically, 'content' and 'accuracy' reflect Perceived Usefulness, while 'format' and 'timeliness' reflect Perceived Ease of Use. A quantitative survey involving 272 Generation Z respondents in Indonesia was analyzed using Covariance-Based Structural Equation Modeling (CB-SEM) with AMOS. The results reveal that Perceived Ease of Use significantly influences both Perceived Usefulness and Attitude Toward Using. Consequently, Perceived Usefulness acts as a partial, rather than full, mediator between ease of use and user attitude. Furthermore, Attitude Toward Using emerged as a powerful determinant of Actual System Use, with the proposed model explaining 89% of its variance (R2 = 0.89). These findings suggest a synergistic effect for "digital natives": while an intuitive format and fast response times directly foster positive attitudes, the epistemic quality and accuracy of the AI remain the dominant drivers of sustained engagement. This study contributes theoretically by validating a robust, multicollinearity-resistant modified TAM for conversational AI, providing practical insights for developers to maintain frictionless interfaces while prioritizing algorithmic accuracy to enhance user adoption.
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