Impact of Motivation Factors for Using Generative AI Services on Continuous Use Intention: Mediating Trust and Acceptance Attitude

Sangbum Kang, Yongjoo Choi and Boyoung Kim ()
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Sangbum Kang: Seoul Business School, aSSIST University, Seoul 03767, Republic of Korea
Yongjoo Choi: Seoul Business School, aSSIST University, Seoul 03767, Republic of Korea
Boyoung Kim: Seoul Business School, aSSIST University, Seoul 03767, Republic of Korea

Social Sciences, 2024, vol. 13, issue 9, 1-18

Abstract: This study aims to empirically analyze the relationship between the motivational factors of generative AI users and the intention to continue using the service. Accordingly, the motives of users who use generative AI services are defined as individual, social, and technical motivation factors. This research verified the effect of these factors on intention to continue using the services and tested the meditating effect of trust and acceptance attitude. We tested this through verifying trust and acceptance attitudes. An online survey was conducted on language-based generative AI service users such as OpenAI’s ChatGPT, Google Bard, Microsoft Bing, and Meta-Lama, and a structural equation analysis was conducted through a total of 356 surveys. As a result of the analysis, individual, social, and technical motivational factors all had a positive (+) effect on trust and acceptance attitude on the attitude toward accepting generative AI services. Among them, individual motivation such as self-efficacy, innovation orientation, and playful desire were found to have the greatest influence on the formation of the acceptance attitude. In addition, social factors were identified as the factors that have the greatest influence on trust in the use of generative AI services. When it comes to using generative AI, it was confirmed that social reputation or awareness directly affects the trust in usability.

Keywords: generative AI; motivations; continuous use intention; trust; acceptance attitude (search for similar items in EconPapers)
JEL-codes: A B N P Y80 Z00 (search for similar items in EconPapers)
Date: 2024
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