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| 研究生： |
黃傲晴 Wong, Ngo-Ching |
|---|---|
| 論文名稱： |
AI 聊天機器人的語碼轉換行為對信任及持續互動意願的影響 Code-Switching Behaviour of AI Chatbots and Its Effects on Trust and Willingness to Engage |
| 指導教授： |
陳宜秀
Chen, Yi-Hsiu |
| 口試委員: |
侯宗佑
Hou, Tsung-Yu 余能豪 Yu, Neng-Hao |
| 學位類別： |
碩士
Master |
| 系所名稱： |
創新國際學院 - 全球傳播與創新科技碩士學位學程 Master’s Program in Global Communication and Innovation Technology |
| 論文出版年： | 2026 |
| 畢業學年度： | 114 |
| 語文別： | 英文 |
| 論文頁數： | 127 |
| 中文關鍵詞： | 人工智能聊天機械人 、語碼轉換 、信任 、互動意願 、人機互動 、香港 、溝通適應理論 |
| 外文關鍵詞： | AI Chatbots, Code-Switching, Trust, Willingness to Engage, Human–AI Interaction, Hong Kong, Communication Accommodation Theory |
| 相關次數： | 點閱：16 下載：1 |
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本研究探討人工智慧聊天機械人的語碼轉換（code-switching）行為如何影響香港使用者的信任與互動意願。語碼轉換於世界各地皆常見的溝通方式，通常與本地身份認同、社會歸屬感以及語言靈活性有關。本研究基於溝通適應理論（Communication Accommodation Theory）、人工智慧代理信任研究，以及人機互動相關文獻，本研究旨在探討一個使用粵英語碼轉換的 AI 聊天機械人，是否會比使用非語碼轉換風格的聊天機械人更容易被視為值得信任及更具互動吸引力。
本研究以香港和粵英語間的混用為背景，採單因子組間實驗設計（between-subjects experimental design）。參與者被隨機分配至語碼轉換聊天機械人組別或非語碼轉換聊天機械人組別。聊天機械人的諮詢內容圍繞香港公共房屋計劃，此為一個具嚴肅性及制度性背景的議題。完成互動後，參與者須填寫問卷，以衡量其對聊天機械人的信任及互動意願。
與原先假設相反，研究結果顯示語碼轉換並未提升參與者的信任或互動意願。相反，非語碼轉換組別的參與者對聊天機械人的整體信任顯著較高。控制組聊天機械人亦被認為較具經驗、更理性、更誠實、更具同理心、更敏感及更有人情味。進一步分析發現，性別、教育背景、對 AI 聊天機械人的既有態度，以及語碼轉換頻率，均會影響信任與互動意願的不同面向。特別是，實驗條件對性別與認知信任及情感信任之間的關係具有調節作用。
研究結果顯示，語言上的熟悉感並不會自動轉化為對 AI 系統的信任。雖然語碼轉換在人際溝通中可能有助建立團結感與親近感，但當 AI 在專業諮詢情境中使用語碼轉換時，反而可能違反使用者對正式性、權威性及可靠性的期待。本研究透過展示文化適應型聊天機械人設計除了需考慮本地語言習慣外，亦必須考慮任務嚴肅性、制度角色以及使用者期待，從而對多語人機互動研究作出貢獻。
This study investigates how code-switching behaviour in AI chatbots influences user trust and willingness to engage in the context of Hong Kong. Code-switching between two languages is a ubiquitous communicative practice around the world, including Hong Kong, often associated with local identity, social belonging, and linguistic flexibility. Drawing on Communication Accommodation Theory, research on trust in AI agents, and literature on human–AI interaction, this study examines whether an AI chatbot that performs Cantonese–English code-switching is perceived as more trustworthy and engaging than a chatbot using a non-code-switching style.
A single-factor between-subjects experimental design was adopted. Participants were assigned to interact with either a code-switching chatbot or a non-code-switching chatbot. The chatbot consultation concerned the Hong Kong public housing scheme, a serious and institutionally framed topic. After the interaction, participants completed a survey measuring trust and willingness to engage.
Contrary to the hypotheses, the findings show that code-switching did not increase trust or willingness to engage. Instead, participants in the non-code-switching condition reported significantly higher overall trust. The control chatbot was perceived as more experienced, rational, honest, empathetic, sensitive, and personal. Further analyses revealed that gender, education background, prior attitudes towards AI chatbots, and code-switching frequency shaped specific dimensions of trust and engagement. In particular, experimental condition moderated several gender differences in cognitive and affective trust.
These findings suggest that linguistic familiarity does not automatically enhance trust in AI systems. While code-switching may enhance solidarity in human communication, its use by AI in a professional consultation may violate expectations of formality, authority, and reliability. The study contributes to research on multilingual human–AI interaction by demonstrating that culturally adaptive chatbot design must consider not only local language practices, but also task seriousness, institutional role, and user expectations.
1. Introduction 1
1.1 Research Background and Motivation 1
1.2 Literature and Research Gap 2
1.3 Research Objectives 3
1.3.1 Research Questions 4
1.4 Significance of the Study 4
1.5 Structure of the Thesis 5
2. Literature Review 6
2.1 Code-Switching 8
2.1.1 Definition 8
2.1.2 Research History 8
2.2. Types 9
2.3 Characteristics 9
2.3.1 Matrix Language Hypothesis 10
2.3.2 Blocking Hypothesis 11
2.3.3 Embedded Language Island Trigger Hypothesis 11
2.4 Code-Switchers – Who are They? 11
2.4.1 Language Proficiency 12
2.4.2 Educational Background 12
2.4.3 Age 13
2.5 Why do people do code-switching? 14
2.5.1 Social Relationship Perspective: Communication Accommodation Theory 14
2.5.2 Convergence 15
2.5.3 Divergence 15
2.5.4 Maintenance 15
2.6 Language Behaviour Reflecting Social Relationship 16
2.6.1 Short-Term and Long-Term Accommodation 16
2.6.2 Linguistic Adjustments 17
2.6.3 Paralinguistic Adjustments 19
2.7 Effects on Social Relationships 20
2.8 Code-Switching in Hong Kong: Cantonese and English 22
2.8.1 Historical Background of Language Environment in Hong Kong 22
2.8.2 Linguistic Features 22
2.8.3 Characteristics of Hong Kong Code-Switching 23
2.9 AI Chatbots 24
2.9.1 Conversations with AI Chatbots 24
2.9.2 Differences between Human-AI and Human-Human Conversations 24
2.9.3 Why are they different? 24
2.9.4 How are they different? 25
2.9.5 Can AI Switch Language Codes Like Humans Do? 25
2.9.6 Effect of AI Code-Switching on Mental State 26
2.9.7 Trust in Agents 26
2.9.8 Willingness to Engage with AI Agents 28
2.10 Research Questions 29
2.10.1 Theoretical Gap 29
2.10.2 Experiment Hypotheses 30
3. Methodology 31
3.1 Experimental Design 32
3.1.1 Between-Subjects Design 32
3.1.2 The Experiment 32
3.1.3 Independent Variable 33
3.1.4 Dependent Variables 33
3.1.4.1 Trust 33
3.1.4.2 Willingness to Engage 33
3.2 Pilot Study 34
3.2.1 Assumptions to verify 34
3.2.2 Subjects 35
3.2.3 Materials 35
3.2.3.1 Screenshots 35
3.2.3.2 A Questionnaire 35
3.2.4 Recruitment 36
3.2.5 Procedure and Tasks 36
3.2.6 Data Analysis 37
3.2.6.1 Average Score 37
3.2.6.2 f test and t test 37
3.2.7 Results 37
3.3 Formal Experiment 38
3.3.1 Subjects and Criteria 38
3.3.2 Materials and Setting 39
3.3.3 System Design and Equipment 39
3.3.4 Procedure 42
3.3.5 Post-experiment Survey 43
4. Results 44
4.1 Demographics of the Participants 45
4.2 Reliability Analysis 45
4.3 Main Effects of Experimental Condition 46
4.3.1 Trust Level 46
4.3.2 ANCOVA Controlling for Potential Covariates 48
4.4 Effects of Gender 49
4.4.1 Independent Samples t-Tests 49
4.4.2 ANCOVA Controlling for Potential Covariates 50
4.5 Interaction Effects between Experimental Condition and Gender 52
4.6 Effects of Education Background 59
4.6.1 One-Way ANOVA 59
4.6.2 ANCOVA Controlling for Potential Covariates 60
4.6.3 Interaction Effects Between Experimental Condition and Education 60
4.7 Code-Switching Frequency 61
4.7.1 Code-Switching Frequency and Trust 61
4.8 Correlation Analysis 63
4.9 Multiple Linear Regression on Trust and Willingness to Engage 66
4.10 Section Summary 67
5. Discussion 69
5.1 Code-Switching did not increase Trust 70
5.2 Why Code-Switching may fail in Human–AI Interaction 71
5.3 Trust was more sensitive than Willingness to Engage 73
5.4 Gender Difference in Code-Switching Strategy 74
5.5 Education and Critical Evaluation of AI Chatbots 76
5.6 Prior Attitudes were a Strong Foundation 77
5.7 Code-Switchers also prefer Chatbots who do not code-switch 78
5.8 Reinterpreting the Rejected Hypotheses 79
5.9 Practical Implications for AI Chatbot Design 80
6. Conclusion 82
6.1 Summary of The Study 83
6.2 Answers to the Research Questions 83
6.3 Theoretical Implications 84
6.4 Practical Implications 85
6.5 Limitations 85
6.6 Directions for Future Research 87
6.7 Final Conclusion 88
References 89
Appendix 99
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