Analisis Niat Penggunaan Berkelanjutan AI Design Tools pada Aplikasi Figma Make Melalui Integrasi TAM-ECM dengan Faktor Eksternal UX

Abstract

The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed UI/UX design practices, particularly through the emergence of AI-powered design tools such as Figma Make. Despite their growing adoption, empirical studies examining the determinants of continuance intention toward AI design tools remain limited, especially among Generation Z, a user group characterized by rapid technology adoption and high experiential expectations. To address this gap, this study integrates the Technology Acceptance Model (TAM) and the Expectation Confirmation Model (ECM) with external User Experience (UX) factors while incorporating key characteristics of AI design tools. The study aims to provide a comprehensive understanding of the factors influencing users’ continuance intention to use AI design tools within the Figma Make application. Data were collected from 124 active Figma Make users and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS. The results show that Emotional Experience (EE) has a significant direct effect on Continuance Intention (CI) (β = 0.268, p = 0.001), followed by Interactive Experience (IE) (β = 0.193, p = 0.024), Perceived Intelligence (PI) (β = 0.215, p = 0.034), and Satisfaction (SAT) (β = 0.243, p = 0.019). Confirmation of Expectation (CE) influences CI indirectly through Satisfaction, supporting the post-adoption mechanism proposed by ECM. In addition, Perceived Intelligence significantly affects Perceived Trust (PT), highlighting the importance of perceived AI capability in building user trust. The structural model demonstrates marginal fit (SRMR = 0.123; R2 = 0.474; GoF = 0.566), suggesting that future studies may improve model robustness through larger sample sizes and a more parsimonious model structure. Eleven indicators were also identified as priority areas for improvement. Overall, this study enriches continuance intention research by extending TAM–ECM with UX and AI-related factors while providing practical insights for improving AI design tools and sustaining long-term user engagement.