대화형 생성AI 서비스 사용자의 지속사용의도에 관한 연구: 과업-기술적합(TTF)과 신뢰를 중심으로

This study identified factors related to the technological characteristics of conversational generative AI services and the user's task characteristics. Then, it analyzed the effects of task-technology fit on user satisfaction and continued use. The effects of trust, which represents the degree of users' belief in the information provided by generative AI, on task-technology fit, user satisfaction, and user continuance intention were also examined. A survey was conducted among users of various age groups, and 198 questionnaires were collected and analyzed using SmartPLS 4.0 to validate the proposed model. As a result of hypothesis testing, it was confirmed that language fluency and interactivity among technology characteristics and ambiguity among task characteristics significantly affect user satisfaction and intention to continue using via task-technology fit. However, creativity among skill characteristics and time flexibility among task characteristics did not significantly affect task-technology fit, and trust did not directly affect task-technology fit and intention to continue using, but only positively affected user satisfaction. The results of this study can provide meaningful implications for vendors who want to develop and provide conversational generative AI services or companies who want to adopt generative AI technology to improve business productivity. 1)