Earticle

초록

영어

As financial institutions transition from traditional robo-advisors to sophisticated multi-modal AI technologies, understanding consumer reactions to these new systems, particularly ChatGPT, is crucial. This paper examines the influence of ChatGPT's personalized investment suggestions, human-like empathy, and continuous improvement on consumer perceptions of authenticity and their practical attitudes towards AI-driven financial advice. Utilizing a research framework that combines Service-Dominant Logic (SDL) and Artificial Intelligence Device Use Acceptance (AIDUA), we analyze responses from 822 mobile banking users through structural equation modeling. Our findings indicate that ChatGPT’s advanced capabilities significantly enhance consumers' perceptions of authenticity, which in turn positively affects their utilitarian attitudes, driving both engagement and resistance in financial advisory contexts. This study not only fills a crucial gap by linking AI features with consumer trust and response but also outlines significant implications for integrating ChatGPT into financial services, emphasizing the need for financial institutions to adopt these technologies to improve customer experience and service delivery.

목차

Introduction
Literature Review
Evolution of Financial Advisory Services: From Robo-Advisors to Multi-modal AI
ChatGPT’s Attributes: Personalized InvestmentSuggestion, Human-like Empathy, and ContinuousImprovement
Perceived Authenticity of ChatGPT
Utilitarian Attitude Towards ChatGPT and ConsumerResponse
AI Literacy
Service-Dominant Logic (SDL) and Artificial IntelligentDevice Use Acceptance (AIDUA)
Integrating SDL and AIDUA to UnderstandConsumer-AI Interaction
Hypotheses Development and Research Model
Personalized investment suggestion, Human-likeempathy, Continuous improvement
Perceived authenticity
Utilitarian attitude
AI Literacy
Research Methodology and Research Design
Questionnaire Design and Measurements
Sampling and Data Collection
Data Analysis and Results
Measurement Model
Structural Model
Conclusion and Discussion
Theoretical Implications
Practical Implications
Limitations and future study
References

저자

• 양금 [ 동국대학교 정보경영학과 ]
• 이영찬 [ 동국대학교 정보경영학과 ]

참고문헌