A Study on the Impact of Information and System Characteristics of Generative AI on Continuous Usage Intention

Abstract: Generative AI, an innovative technology that automatically generates various data such as text, images, and speech to assist decision-making, has gained significant attention. This highlights the need for research on how the information characteristics and system characteristics of generative AI influence user experience and the intention for continued use. Therefore, this study aims to analyze the factors influencing the adoption of user experience and continuous use intention based on the Technology Acceptance Model (TAM), focusing on the informational and system characteristics of generative AI. A survey was conducted with 136 users who had prior experience using generative AI to assist in decision-making, and Smart PLS 4.0 was employed for analysis. The findings reveal that informational characteristics significantly impact user experience and continuous usage intention. Among these, timeliness and accuracy were identified as critical factors that enhance reliability and increase user satisfaction. However, the hypothesis that personalization, as part of system characteristics, significantly affects user experience was rejected, suggesting potential concerns over privacy and a lack of trust in data. Additionally, social presence positively influenced user experience, while perceived risk had a negative impact. This study underscores the importance of evaluating the efficiency of personalization strategies and strengthening security and privacy in the design of generative AI services. Academically, it provides an integrated validation of the informational and system characteristics of generative AI, addressing gaps in existing literature. Practically, it suggests enhancing user trust by refining personalization strategies and improving data security and transparency. Nevertheless, this study is limited by a sample skewed toward specific age groups and occupations, highlighting the need for future research involving diverse industries and user demographics.