Analyzing the impact of technical and user character istics of generative AI on Chinese users' intentions = 생성형 AI의 기술적 특성과 사용자 특성이 중국 사용자의 의도에 미치는 영향 분석

다국어 초록 (Multilingual Abstract)

Analyzing the Impact of Technical and User Character istics of Generative AI on the Intentions of Chinese Users Yue Li The Graduate School Department of AI Content Convergence Hoseo University Korea （Supervised by professor Jungmann Lee） In the 21st century, the rapid development of generative AI has led to its rapid development in the fields of text synthesis, visual content generation, and natural language processing. Generative AI technology has regenerated the human-computer interaction model and has had a profound impact on the user's cognitive and behavioral patterns. It has also created good conditions for intelligent systems designed for autonomous content creation and user participation. Generative AI has improved creative ability and operational efficiency, and has also provided a generative interactive experience in AI professional and practical aspects. This study focuses on generative AI and In recent years, with the rapid development of generative AI technology, its application in many fields such as education, creation, office, and media has become increasingly widespread, and the frequency of use and dependence of users have also increased significantly. However, how the interactive relationship between the technical characteristics and their own characteristics experienced by users in the process of using generative AI affects their subjective perception and continuance intention （CI） still lacks systematic theoretical research and empirical verification. Therefore, this study takes the technology acceptance model （TAM） as the theoretical basis, combines the unique technical attributes of generative AI, and constructs an extended model of ＂AI technical characteristics-user characteristics-perceived usefulness and perceived ease of use-continuance intention （CI）＂. In addition, the study combines actual application scenarios, summarizes typical cases and best practices of generative AI, and proposes strategies for optimizing interactive design, providing theoretical support and practical reference for the continued development and widespread application of generative AI technology. The widespread application of generative AI not only optimizes production efficiency, but also creates a new form of user interaction experience, meeting user needs in a more creative and emotional way.

목차 (Table of Contents)

• Ⅰ. Introduction 1
• 1.1 Research Background and The Purpose of Research 1
• 1.1.1 Research Background 1
• 1.1.2 The Purpose of Research 3
• 1.1.3 Research Implications 5
• 1.2 Research Methodology and Tools 6
• 1.2.1 Research Methodology 6
• 1.2.2 Research Tools 7
• 1.3 Research Contents and Innovation 9
• 1.3.1 Research Contents 9
• 1.3.2 Research Innovation 9
• 1.4 Research Questions 10
• Ⅱ. Literature Review 11
• 2.1 Concept and Technological Development of Generative AI 11
• 2.1.1 Definition of Generative AI 11
• 2.1.2 Overview of Key Technology Characteristics 11
• 2.2 Theoretical Review of User Continuing Use Intention 14
• 2.2.1 Concept of Continuity Intention 14
• 2.2.2 Application Cases in the Information System Field 14
• 2.3 Technology Acceptance Theory: TAM Model Centered 14
• 2.3.1 TAM Model Overview 14
• 2.3.2 Definition of the concepts of PU and PEOU 15
• 2.4 Factors Affecting the Use of Generative AI 16
• 2.4.1 Theories Related to Technical Factors 16
• 2.4.2 Theories related to user factors 22
• 2.4.3 Consideration of Cognitive and Motivational Factors 25
• III. Research model and hypotheses 26
• 3.1 Research model composition and description 26
• 3.1.1 Components of the research model 26
• 3.1.2 Theoretical Research Model Presentation 27
• 3.2 Variable Definition and Measurement Items 28
• 3.2.1 Technical Variable Definition 28
• 3.2.2 Definition of user-related variables 31
• 3.2.3 Cognitive Variable Definition 32
• 3.2.4 Definition of outcome variables 33
• 3.3 Research Hypothesis Setting 34
• 3.3.1 Hypothesis between Technology Characteristics and Detected Variables 34
• 3.3.2 Hypothesis between User Characteristics and Detected Variables 39
• 3.3.3 Hypothesis between Detected Variables and Continuous Use Intention 41
• 3.3.4 Mediating variable hypothesis 42
• Ⅳ. Empirical Results 43
• 4.1 Data collection and sample characteristics 43
• 4.1.1 Survey Overview 43
• 4.1.2 Sample Size and Response Processing 44
• 4.1.3 Respondents’ Demographic Characteristics 45
• 4.2 Measurement methods and questionnaires 47
• 4.2.1 Perceived ease of use （PEOU） 48
• 4.2.2 Perceived usefulness （PU） 48
• 4.2.3 Continued Intention （CI） 49
• 4.2.4 Personalization （PI） 50
• 4.2.5 Interactivity （IN） 51
• 4.2.6 Context Awareness （CA） 51
• 4.2.7 Creativity （CR） 52
• 4.2.8 AI Literacy（AL） 52
• 4.2.9 Usage Experience （UE） 52
• 4.2.10 Information Credibility（IC） 53
• 4.3 Model validation analysis 55
• 4.3.1 Model Fit 55
• 4.3.2Reliability 56
• 4.3.3 Confirmatory Factor Analysis 57
• 4.3.4 Analysis of Core TAM Variables （PU, PEOU, CI） 60
• 4.3.5 Construct discriminant validity 62
• 4.3.6 Reliability analysis 64
• 4.3.7 Validity analysis 66
• 4.4 Verification of research hypotheses 67
• 4.4.1 Path Analysis 67
• 4.4.2 Mediation Effect Analysis 71
• 4.4.3 CFA-Based Validation of the Measurement Model 72
• 4.5 Verification of the Structural Model and Hypothesis Testing 73
• 4.5.1 Structural Path Coefficient Analysis 73
• 4.6 Analysis results 76
• Ⅴ. Conclusions and Discussions 79
• References 85
• Appendix: Questionnaire of continuance usage intention of mobile games 90