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題名 為人工智慧歡呼三聲? 探討台灣公務員對採用生成式AI的看法與態度
Three Cheers for AI? Exploring Views and Attitudes Toward the Adoption of Generative AI by Taiwanese Civil Servants作者 林民偉
Lin, Min-Wei貢獻者 陳敦源
Chen, Don-Yun
林民偉
Lin, Min-Wei關鍵詞 人工智慧
生成式AI
使用採納
UTAUT模型
對人工智慧的信任
公共部門
公務員
Artificial intelligence
Generative AI
Use and adoption
UTAUT model
Trust in AI
Public sector
Civil servants日期 2026 上傳時間 2-Mar-2026 12:30:08 (UTC+8) 摘要 生成式人工智慧（generative AI）的最新進展正加速 AI 工具在政府部門中的擴散，然而公共組織內的採用程度仍缺乏深入研究。本論文以臺灣文官體系作為在理論上具啟發性的研究場域，探討公務人員在日常工作中如何評估、採用並信任生成式 AI （如 ChatGPT、Copilot、Gemini）。本研究的核心目標在於辨識生成式 AI 何時成為可實際運用的工作工具，而非僅是一個被廣泛肯定的新科技，並據此檢視官僚組織情境下科技接受模型的理論意涵。 本研究以「整合型科技接受與使用理論」（UTAUT）為基礎，並針對生成式 AI 的特性加以調整。除了 UTAUT 的四個核心構念（績效期望、努力期望、社會影響、促成條件）外，本研究架構亦納入「對生成式 AI 的態度」與「對生成式 AI 的信任」作為與 AI 相關的延伸構念。在實證設計上，本研究採用順序解釋性混合研究設計。量化階段針對台灣公務員進行全國性線上問卷調查，並基於擴展後的 UTAUT 模型進行結構方程模型（SEM）分析。質化階段則透過 12 場半結構式訪談，探討受訪者在實務中如何使用生成式 AI，以及其如何權衡使用所帶來的效益與風險。 量化結果呈現一致的「意圖—使用之謎題」：行為意圖與自陳之生成式 AI 使用呈負向關聯，且 UTAUT 核心構念亦與行為意圖呈負向關聯，與理論預期相左。診斷分析顯示，這種反直覺的模式不易透過替代模型設定而消除。相較之下，信任與行為意圖呈正向關聯，但仍無法化解意圖與使用之間的反轉。訪談結果進一步釐清，為何在公部門組織中，生成式 AI 的採用可能偏離以意圖為核心的模型邏輯。受訪者普遍將生成式 AI 的使用描述為偶發且視任務而定，並強調即使一般使用者對生成式 AI 持正向態度或使用意圖，實際使用度仍伴隨明顯的成本。綜合而言，混合方法證據顯示：行為意圖反映了對生成式 AI 的廣泛認同，而實際使用則主要取決於任務需求、任務—科技契合度、事實查核負擔與組織限制。因此，當生成式 AI 的使用屬於自願性質，且各工作場域在使用的組織實踐、規則與規範上存在差異時，本論文進一步界定了 UTAUT 理論的適用範圍限制。
Recent advances in generative artificial intelligence (gen-AI) are accelerating the diffusion of AI-enabled tools across government, yet the extent of adoption in public organizations remains understudied. This dissertation examines how civil servants evaluate, use, and trust gen-AI chatbots (e.g., ChatGPT, Copilot, Gemini) in daily work, with Taiwan’s civil service providing a theoretically informative setting. The central goal is to identify when gen-AI becomes a practical tool rather than a broadly endorsed innovation, and what this implies for models of technology acceptance in bureaucratic organizations. The dissertation builds on the Unified Theory of Acceptance and Use of Technology (UTAUT) but adapts it to the distinctive properties of gen-AI. In addition to the four core UTAUT constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions), the research framework incorporates attitudes toward gen-AI and trust in gen-AI as AI-relevant extensions that are frequently treated as central to AI acceptance. Empirically, the study employs a sequential explanatory mixed-methods design. The quantitative phase draws on a nationwide online survey of Taiwanese civil servants and estimates a structural equation model (SEM) based on the extended UTAUT model. The qualitative phase consists of 12 semi-structured interviews that examine how respondents use generative AI in practice and how they weigh its benefits and risks. The quantitative results reveal a consistent intention–use puzzle: behavioral intention is negatively associated with reported gen-AI use, and the core UTAUT constructs are also negatively associated with behavioral intention, contrary to theoretical expectations. Diagnostic analyses indicate that this counterintuitive pattern is not readily eliminated by alternative specifications. Trust, by contrast, is positively associated with behavioral intention, but it does not resolve the intention–use reversal. The interviews clarify why gen-AI adoption can deviate from intention-based models in public organizations. Respondents commonly describe gen-AI use as episodic and task-contingent, while also emphasizing practical costs associated with use despite favorable attitudes or stated intention to use gen-AI. Taken together, the mixed-method evidence suggests that behavioral intention captures broad approval of generative AI, whereas actual use is governed by task need, task–technology fit, fact-checking burdens, and organizational constraints. 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國立政治大學
公共行政學系
101256504資料來源 http://thesis.lib.nccu.edu.tw/record/#G0101256504 資料類型 thesis dc.contributor.advisor 陳敦源 zh_TW dc.contributor.advisor Chen, Don-Yun en_US dc.contributor.author (Authors) 林民偉 zh_TW dc.contributor.author (Authors) Lin, Min-Wei en_US dc.creator (作者) 林民偉 zh_TW dc.creator (作者) Lin, Min-Wei en_US dc.date (日期) 2026 en_US dc.date.accessioned 2-Mar-2026 12:30:08 (UTC+8) - dc.date.available 2-Mar-2026 12:30:08 (UTC+8) - dc.date.issued (上傳時間) 2-Mar-2026 12:30:08 (UTC+8) - dc.identifier (Other Identifiers) G0101256504 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/161866 - dc.description (描述) 博士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 公共行政學系 zh_TW dc.description (描述) 101256504 zh_TW dc.description.abstract (摘要) 生成式人工智慧（generative AI）的最新進展正加速 AI 工具在政府部門中的擴散，然而公共組織內的採用程度仍缺乏深入研究。本論文以臺灣文官體系作為在理論上具啟發性的研究場域，探討公務人員在日常工作中如何評估、採用並信任生成式 AI （如 ChatGPT、Copilot、Gemini）。本研究的核心目標在於辨識生成式 AI 何時成為可實際運用的工作工具，而非僅是一個被廣泛肯定的新科技，並據此檢視官僚組織情境下科技接受模型的理論意涵。 本研究以「整合型科技接受與使用理論」（UTAUT）為基礎，並針對生成式 AI 的特性加以調整。除了 UTAUT 的四個核心構念（績效期望、努力期望、社會影響、促成條件）外，本研究架構亦納入「對生成式 AI 的態度」與「對生成式 AI 的信任」作為與 AI 相關的延伸構念。在實證設計上，本研究採用順序解釋性混合研究設計。量化階段針對台灣公務員進行全國性線上問卷調查，並基於擴展後的 UTAUT 模型進行結構方程模型（SEM）分析。質化階段則透過 12 場半結構式訪談，探討受訪者在實務中如何使用生成式 AI，以及其如何權衡使用所帶來的效益與風險。 量化結果呈現一致的「意圖—使用之謎題」：行為意圖與自陳之生成式 AI 使用呈負向關聯，且 UTAUT 核心構念亦與行為意圖呈負向關聯，與理論預期相左。診斷分析顯示，這種反直覺的模式不易透過替代模型設定而消除。相較之下，信任與行為意圖呈正向關聯，但仍無法化解意圖與使用之間的反轉。訪談結果進一步釐清，為何在公部門組織中，生成式 AI 的採用可能偏離以意圖為核心的模型邏輯。受訪者普遍將生成式 AI 的使用描述為偶發且視任務而定，並強調即使一般使用者對生成式 AI 持正向態度或使用意圖，實際使用度仍伴隨明顯的成本。綜合而言，混合方法證據顯示：行為意圖反映了對生成式 AI 的廣泛認同，而實際使用則主要取決於任務需求、任務—科技契合度、事實查核負擔與組織限制。因此，當生成式 AI 的使用屬於自願性質，且各工作場域在使用的組織實踐、規則與規範上存在差異時，本論文進一步界定了 UTAUT 理論的適用範圍限制。 zh_TW dc.description.abstract (摘要) Recent advances in generative artificial intelligence (gen-AI) are accelerating the diffusion of AI-enabled tools across government, yet the extent of adoption in public organizations remains understudied. This dissertation examines how civil servants evaluate, use, and trust gen-AI chatbots (e.g., ChatGPT, Copilot, Gemini) in daily work, with Taiwan’s civil service providing a theoretically informative setting. The central goal is to identify when gen-AI becomes a practical tool rather than a broadly endorsed innovation, and what this implies for models of technology acceptance in bureaucratic organizations. The dissertation builds on the Unified Theory of Acceptance and Use of Technology (UTAUT) but adapts it to the distinctive properties of gen-AI. In addition to the four core UTAUT constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions), the research framework incorporates attitudes toward gen-AI and trust in gen-AI as AI-relevant extensions that are frequently treated as central to AI acceptance. Empirically, the study employs a sequential explanatory mixed-methods design. The quantitative phase draws on a nationwide online survey of Taiwanese civil servants and estimates a structural equation model (SEM) based on the extended UTAUT model. The qualitative phase consists of 12 semi-structured interviews that examine how respondents use generative AI in practice and how they weigh its benefits and risks. The quantitative results reveal a consistent intention–use puzzle: behavioral intention is negatively associated with reported gen-AI use, and the core UTAUT constructs are also negatively associated with behavioral intention, contrary to theoretical expectations. Diagnostic analyses indicate that this counterintuitive pattern is not readily eliminated by alternative specifications. Trust, by contrast, is positively associated with behavioral intention, but it does not resolve the intention–use reversal. The interviews clarify why gen-AI adoption can deviate from intention-based models in public organizations. Respondents commonly describe gen-AI use as episodic and task-contingent, while also emphasizing practical costs associated with use despite favorable attitudes or stated intention to use gen-AI. Taken together, the mixed-method evidence suggests that behavioral intention captures broad approval of generative AI, whereas actual use is governed by task need, task–technology fit, fact-checking burdens, and organizational constraints. The dissertation therefore specifies boundary conditions for UTAUT-style models when the use of generative AI is discretionary, and organizational practices, rules, and norms regarding its use vary across work settings. en_US dc.description.tableofcontents DEDICATION iii ACKNOWLEDGEMENTS v ABSTRACT vii 中文摘要 ix TABLE OF CONTENTS xi LIST OF TABLES xiv LIST OF FIGURES xv CHAPTER 1: INTRODUCTION 1 1.1 Research Background 1 1.1.1 The Emergence of AI in Public Administration 1 1.1.2 Taiwan’s Efforts to Modernize Its Civil Service Through AI 6 1.1.3 The Scope of This Study 9 1.2 Research Objectives 11 1.3 Research Questions 14 1.4 Overview of the Chapters 15 CHAPTER 2: LITERATURE REVIEW 18 2.1 Taiwan’s Digital Government Transformation 18 2.2 The Unified Theory of Acceptance and Use of Technology (UTAUT) and Attitudes toward Generative AI 26 2.3 Trust and Generative AI Adoption 36 2.4 Selected Cross-National Evidence on Generative AI Use in the Public Sector 40 2.5 Chapter Summary 43 CHAPTER 3: RESEARCH METHODS 46 3.1 Research Framework 46 3.2 Research Hypotheses 47 3.2.1 Performance Expectancy 47 3.2.2 Effort Expectancy 48 3.2.3 Social Influence 49 3.2.4 Facilitating Conditions 49 3.2.5 Job Replacement Anxiety 50 3.2.6 Attitudes Toward Generative AI Chatbots 51 3.2.7 Trust in Generative AI Chatbots 52 3.2.8 Behavioral Intention and Use 53 3.3 Measurement and Operationalization of Constructs 57 3.4 Research Design 62 3.5 Data and Sampling 68 3.5.1 Quantitative Phase: TGBS Survey of Taiwanese Civil Servants 68 3.5.2 Qualitative Phase: Semi-structured Interviews 69 CHAPTER 4: RESULTS 75 4.1 Quantitative Results 75 4.1.1 Sample Characteristics 75 4.1.2 Descriptive Statistics of Gen-AI Use 77 4.1.3 SEM Estimation Strategy and Decision Rules 83 4.1.4 Measurement Model 84 4.1.5 Structural Model 89 4.1.6 Diagnostic and Alternative Model Specifications 93 4.1.7 Section Summary 103 4.2 Qualitative Results 105 4.2.1 Theme 1. Trust in Generative AI: “Conditional Trust” and the Need for Additional Verification 106 4.2.2 Theme 2: Experience With Generative AI: Heterogeneous Use and Task-Driven Adoption 111 4.2.3 Theme 3. Job Anxiety: Replacement Fears Are Often Displaced Into “Competitiveness” and “Misuse” Concerns 115 4.2.4 Theme 4. Support for AI Development: Broad Endorsement with Governance Conditions and “Sovereign AI” Considerations 119 4.2.5 Theme 5. Explaining the Behavioral Intention–Use Puzzle: Need, Skills, and Expressive Responding 122 4.3: Integration and Discussion: Explaining the Intention–Use Puzzle 128 4.3.1 Suspect 1: Measurement Problems 128 4.3.2 Suspect 2: Theory Choice (i.e., UTAUT May Be Incomplete) 129 4.3.3 Suspect 3: Technology-Specific Properties of Generative AI 130 4.3.4 Suspect 4: The Public Sector Context 131 4.3.5 Integrated Implications 132 CHAPTER 5: CONCLUSION 134 5.1 Summary and Contributions of the Study 134 5.2 Limitations 136 5.3 Future Research and Practical Implications 138 REFERENCES 142 APPENDIX 166 A. 行政院及所屬機關（構）使用生成式AI參考指引 (Guidelines for the Executive Yuan and Affiliated Agencies on Using Generative Artificial Intelligence) 166 B. Codebook and Operationalization of All Survey Questions and Items from the 2025 Taiwan Government Bureaucrat Survey-Wave 10 168 C. Interview Protocol for Generative AI Use in Taiwanese Civil Service 176 D. Descriptive Statistics for All Variables 180 E. Response to Dissertation Committee Members’ Suggestions 183 zh_TW dc.format.extent 2399282 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0101256504 en_US dc.subject (關鍵詞) 人工智慧 zh_TW dc.subject (關鍵詞) 生成式AI zh_TW dc.subject (關鍵詞) 使用採納 zh_TW dc.subject (關鍵詞) UTAUT模型 zh_TW dc.subject (關鍵詞) 對人工智慧的信任 zh_TW dc.subject (關鍵詞) 公共部門 zh_TW dc.subject (關鍵詞) 公務員 zh_TW dc.subject (關鍵詞) Artificial intelligence en_US dc.subject (關鍵詞) Generative AI en_US dc.subject (關鍵詞) Use and adoption en_US dc.subject (關鍵詞) UTAUT model en_US dc.subject (關鍵詞) Trust in AI en_US dc.subject (關鍵詞) Public sector en_US dc.subject (關鍵詞) Civil servants en_US dc.title (題名) 為人工智慧歡呼三聲? 探討台灣公務員對採用生成式AI的看法與態度 zh_TW dc.title (題名) Three Cheers for AI? Exploring Views and Attitudes Toward the Adoption of Generative AI by Taiwanese Civil Servants en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Ahn, M. J., and Chen, Y. C. (2022). 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