Earticle

초록

영어

This study aims to identify the factors affecting generative AI satisfaction and continuous use intention through an empirical analysis using a comparative study of South Korea and Japan. To verify the research model, data were collected from 151 university students each in South Korea and Japan, and based on this, an analysis was conducted using Structural Equation Modeling (SEM). The analysis results are as follows: First, information quality was found to have a significant positive effect on generative AI satisfaction in both the Korean and Japanese samples. Second, interactivity did not affect generative AI satisfaction in the Korean sample, but it showed a significant positive effect on generative AI satisfaction in the Japanese sample. Third, performance expectancy was found to have a significant positive effect on generative AI satisfaction in both the Korean and Japanese samples. Fourth, generative AI satisfaction was found to have a significant positive effect on continuous use intention in both the Korean and Japanese samples. Fifth, the results of the indirect effect analysis revealed that in the Korean sample, only information quality and performance expectancy had a significant positive effect on continuous use intention through generative AI satisfaction; however, in the Japanese sample, information quality, interactivity, and performance expectancy all had a significant positive effect on continuous use intention through generative AI satisfaction. The results of this study hold important significance in terms of being a comparative study between South Korea and Japan, and they emphasize that the characteristics of information quality, interactivity, and performance expectancy can affect generative AI satisfaction and continuous use intention.

목차

Abstract
1. Introduction
2. Theoretical Background
2.1 Information Quality
2.2 Interactivity
2.3 Performance Expectancy
2.4 Generative AI Satisfaction
2.5 Continuous Use Intention
3. Methodology
3.1 Establishment of the Research Model and Research Questions
3.2 Operational Definition and Questionnaire Composition
3.3 Data Collection and Analysis Methods
4. Empirical Analysis
4.1 Demographic Characteristics
4.2 Measurement Scales and Scale Evaluation
4.3 Evaluation of the Research Model and Comparison of Hypothesis Testing Analysis Results Between Countries
5. Discussion and Conclusion
References

키워드

저자

• Park Se Rin [ Seoul National University TEMEP College of Engineering ]
• Park Jun Cheul [ Kangwon National University Department of Industrial and Management Engineering ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2026

• 십진분류

  KDC 325 DDC 658