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| 研究生： |
李新怡 Nalinkarn Deesaovapak |
|---|---|
| 論文名稱： |
人工智慧個人理財應用程式中解釋深度對適當依賴、信任與感知價值之影響 The Effects of Explanation Depth in AI-Based Personal Finance Apps on Appropriate Reliance, Trust and Perceived Value |
| 指導教授： | 張君豪 |
| 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： | 67 |
| 中文關鍵詞： | 可解釋人工智慧（XAI） 、適當依賴 、自動化偏誤 、對人工智慧的信任 、人機協作決策 、個人理財應用程式 |
| 外文關鍵詞： | explainable AI (XAI), appropriate reliance, automation bias, trust in AI, humanAI decision-making, personal finance apps |
| 相關次數： | 點閱：10 下載：0 |
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本研究探討了消費型金融科技（Fintech）應用程式介面上，可解釋人工智慧（XAI）的說明深度對使用者行為與態度所產生的影響。本研究採用受試者間線上實驗設計（受試者總數 $N = 182$），將零售預算管理 App 的消費者隨機分配至三種說明風格小組：黑盒模型（無說明）、簡短單句說明，以及詳細的多句情境解析。受試者需評估八個預算警示情境，其中包含平衡配比的正確與錯誤 AI 建議（含有明確的系統缺陷）。多變量變異數分析（MANOVA）證實了跨結果變數的顯著多變量效果（Pillai's Trace = .117, $F(8, 354) = 2.76$, $p = .006$）。隨後的追蹤檢定揭示了令人矚目的「信任與準確度悖離」現象：雖然主觀信任度、實用性與透明度評分隨著說明密度的增加呈線性成長，但客觀的行為決策品質卻顯著下降。詳細的說明嚴重損害了整體「適當依賴度」（Appropriate Reliance, AoR），導致使用者批判性拒絕錯誤建議的能力大幅重挫 21.95 個百分點，進而引發了由理由說明所驅動的自動化偏誤（automation bias）。此外，基礎金融素養並未展現出顯著的調節效應。介面設計師必須部署漸進式資訊揭露模型與主動式不確定性指標，而非僅提供靜態的文字理據，才能有效對抗使用者的過度信任問題。
This study explores the behavioral and attitudinal consequences of Explainable AI (XAI) explanation depth in consumer fintech app interfaces. Using an online betweensubjects experiment (N = 182), retail budgeting app consumers were randomly assigned to three explanation styles: black-box (no explanation), a brief single-sentence explanation, or a detailed multi-sentence contextual breakdown. Participants evaluated eight budget-alert vignettes containing a balanced split of correct and incorrect AI advice featuring explicit system flaws. A multivariate analysis of variance (MANOVA) confirmed a significant multivariate effect across outcomes, Pillai's Trace = .117, F(8, 354) = 2.76, p = .006. Follow-up tests revealed a remarkable trust-accuracy divergence: while subjective trust, usefulness, and transparency ratings increased linearly with explanation density, objective behavioral decision quality suffered. Detailed explanations severely degraded overall Appropriate Reliance (AoR) by driving a massive 21.95 percentage point collapse in users' capacity to critically reject bad advice, triggering a justification-driven automation bias. Baseline financial literacy yielded no significant moderating effects. Interface designers must deploy progressive disclosure models and active uncertainty indicators rather than static text rationales to counteract user over-trust.
Acknowledgement i
Abstract ii
Table of Contents iii
1. Introduction 1
1.1 Background of the Study 1
1.2 Problem statement 1
1.3 Research motivation 2
1.4 Research Questions 4
2. Theoretical Background 5
2.1 Trust Calibration Theory and Appropriate Reliance (AoR) 5
2.2 Trust in AI and the Role of Explainable AI (XAI) 5
2.3 Cognitive Load Theory & the AI Transparency Dilemma 6
2.4 Technology Acceptance Model (TAM) & Perceived Usefulness 7
2.5 Financial Literacy as a Moderator 8
3. Methodology 9
3.1 Participants 9
3.2 Experimental Design 11
3.3 Procedure 16
3.4 Instruments 17
3.5 Data Analysis 18
3.6 Ethical Considerations 20
4. Result 21
4.1 Data Cleaning and Final Sample 21
4.2 Reliability Analysis 21
4.3 Randomization Check 22
4.4 Manipulation Check 22
4.5 Statistical Assumption Checks 23
4.6 Multivariate Analysis 23
4.7 Main Analyses: Effects of Explanation Depth 23
4.7.1 RQ1: Explanation Depth and Appropriate Reliance 23
4.7.2 RQ2: Explanation Depth and Trust 25
4.7.3 RQ3: Explanation Depth and Perceived Usefulness 25
4.7.4 RQ4: Explanation Depth and Perceived Transparency 25
4.8 Moderation Analyses: RQ5 and RQ6 28
4.8.1 RQ5: Financial Literacy as Moderator of Explanation Depth on AoR 28
4.8.2 RQ6: Financial Literacy as Moderator of Explanation Depth on Trust 28
5. Discussion 30
5.1 Interpretation of the Manipulation Check 30
5.2 Discussion of Main Effects 30
5.2.1 RQ1: Explanation Depth and Appropriate Reliance 30
5.2.2 RQ2: Explanation Depth and Trust 30
5.2.3 RQ3: Explanation Depth and Perceived Usefulness 31
5.2.4 RQ4: Explanation Depth and Perceived Transparency 31
5.3 Multivariate Effect 32
5.4 Moderation by Financial Literacy 32
6. Conclusion and Recommendation 34
6.1 Conclusion 34
6.2 Limitations 34
6.3 Recommendation 35
Reference 37
Appendix A 42
Appendix B 44
Appendix C 46
Appendix D 48
Appendix E 52
Appendix F 53
Appendix G 55
Appendix H 56
Appendix I 57
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