COMPARATIVE UX ANALYSIS OF GENERATIVE AI IMAGE USING UTAUT2 AND CONCURRENT THINK ALOUD

Abstract - Generative AI image platforms are gaining popularity for digital content generation but user adoption and actual usability is highly varied from system to system. Three platforms Imagen, DALL-E, and Leonardo. Ai are the focus of this study through a mixed-method research design model: the UTAUT2 model, SEM-PLS model and the Concurrent Think Aloud (CTA) method. 90 respondents were sampled for the quantitative phase and 15 professional designers for the qualitative CTA session. Results from SEM-PLS indicate that Habit (HT) is the best predictor of Behavioural Intention on all platforms. For Imagen and Leonardo. Ai, habit was the only important factor that showed dependence on habitual practice rather than perceived usefulness. DALL-E exhibited a more robust acceptance model, that is, it demonstrated significant impacts on Use Behaviour (UB) from Effort Expectancy (EE) and Behavioural Intention (BI). CTA analysis showed significant problems Imagen had difficulty understanding Indonesian language prompts, while DALL-E had slow-generating and unreliable tasks and Leonardo. Ai also showed basic prompts comprehension issues. Linking these two examples tells us that these two data sets is a huge gulf between perceived ease of use (quantitative), and the sort of real-time user experience described as perceived through qualitative eyes (the time-based user experience). This paper provides new insights on the adoption of generative AI and emphasizes the need for providing additional multilingual processing, system stability and increased interface feedback to improve user experience. Keywords — Concurrent Think Aloud, DALL-E, Generative AI, Imagen, Leonardo. Ai, SEM-PLS, UTAUT2