This study is the result of a collaborative effort by Professor Jang Hyun Kim of the School of Global Convergence and members of the Data Science & Social Analytics Lab (DSSAL), including Dongyan Nan (Ph.D. graduate, now Assistant Professor at Macau University of Science and Technology), Seungjong Sun (Ph.D. candidate), Shunan Zhang (Ph.D. graduate, now Fellow at Huaqiao University in China), and Xiangying Zhao (Ph.D. graduate).

The purpose of this research is to gain an in-depth understanding of user behavior toward Generative Artificial Intelligence. Focusing on ChatGPT as a representative example, the study identifies key factors that influence users’ continued usage intention and recommendation intention. Theoretically, it proposes a new integrated model that extends the Expectation Confirmation Model (ECM) by incorporating Information System Success Theory (ISST), privacy concerns, and perceived innovativeness. This approach addresses the limitations of prior studies, which largely focused on initial usage intention, and instead highlights both cognitive and emotional determinants of post-adoption behavior—providing meaningful academic contributions.

A total of 252 Korean ChatGPT users participated in an online survey, and the results were analyzed using structural equation modeling. The findings show that the proposed integrated model effectively explains users’ continued use and recommendation behaviors. Information Quality and System Quality emerged as core variables that enhance both types of behavioral intentions by strengthening confirmation, perceived usefulness, and satisfaction.

Perceived innovativeness also had a positive effect on user satisfaction, demonstrating that users form more favorable experiences when they view ChatGPT as a creative and cutting-edge technology. Conversely, privacy concerns negatively affected satisfaction, although the impact was relatively small—suggesting that users may be willing to accept certain privacy risks in exchange for convenience and utility.

Based on these findings, the study offers practical implications for promoting the adoption of generative AI services. Service providers can enhance user engagement by improving model accuracy and stability to reduce information bias, designing user-friendly interfaces, and effectively promoting the creativity and innovativeness of AI technologies.

Professor Kim noted, “By comprehensively analyzing the determinants of continued use and recommendation of generative AI, this study offers new insights into user experience–based AI adoption research. We plan to further advance the model by expanding our research to include voice- and image-based generative AI in the future.” His research team continues to explore the intersection of AI and user experience (UX) and has published numerous SSCI/SCIE-indexed papers in related fields.

※ Title: Analyzing behavioral intentions toward Generative Artificial Intelligence: the case of ChatGPT

※ Journal: Universal Access in the Information Society

※ Link: https://doi.org/10.1007/s10209-024-01116-z

※ Portal(Pure): https://pure.skku.edu/en/persons/janghyun-kim/

Dongyan Nan(Ph.D. graduate, now Assistant Professor at Macau University of Science and Technology), Shunan Zhang(Ph.D. graduate, now Fellow at Huaqiao University in China), Xiangying Zhao(Ph.D. graduate)