Earticle

초록

영어

Drawing on theories from personality psychology and motivation, this study examined the acceptance of generative Artificial Intelligence (AI) technology. It analyzed the impact of demographic characteristics, usage level of generative AI, user personality traits, and motivation on trust, satisfaction, and intention to use (including willingness to pay) for the service. An online survey was conducted targeting users with experience in generative AI (N = 400). The results indicated that functional motivation directly influenced trust, satisfaction, and intention to use when the AI service was both free and paid. Hedonic motivation affected trust, satisfaction, and intention to use when the AI service was free and significantly influenced intention to use when it was paid, only when mediated by trust. Frequency of use directly impacted both intentions. Among personality factors, agreeableness, conscientiousness, and extraversion impacted the intention to use AI, both paid and free, when mediated by trust. Satisfaction directly influenced the intention to use when the AI service was free but did not exhibit direct or indirect effects on the intention to use for paid AI services. Conclusion: These findings present valuable insights for researchers and practitioners, aiding in understanding individual acceptance of generative AI technology based on personal characteristics and suggesting practical implications for marketing strategies, such as the development of generative AI features and personalized messages.

목차

Abstract
1. Introduction
2. Theoretical background & Literature review
2.1. Theoretical discussion on the emergence of AI and technology acceptance
2.2. Influence of personality on technology acceptance
2.3. Functional and hedonic motivation
2.4. Trust and satisfaction in the acceptance of information technology
2.5. Purchase intention and continued intention to use
2.6. Hypothesis development
3. Methods
3.1. Measurement variables and operational definitions
3.2. Data collection and sample characteristics
4. Results
4.1. Exploratory factor analysis (EFA)
4.2. Hypothesis testing
5. Discussion and conclusion
Acknowledgement
References

키워드

저자

• Sunyoung Bak [ Doctoral student, School of Media and Communication, Korea University, Seoul, Korea ]
• Sungtae Kim [ Professor, School of Media and Communication, Korea University, Seoul, K ]
• Brandon Hwansun Lee [ M.S, School of Media and Communication, Korea University, Seoul, Korea ]
• Saeyeong Kim [ M.S, School of Media and Communication, Korea University, Seoul, Korea ]
• Somin Park [ M.S, School of Media and Communication, Korea University, Seoul, Korea ]
• Jiyoun Seok [ Doctoral student, School of Media and Communication, Korea University, Seoul, Korea ]
• Dongmi Kim [ Doctoral student, School of Media and Communication, Korea University, Seoul, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380