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研究生： 張勝嘉
Chang, Sheng-Chia
論文名稱： 人工智慧創作時代下的情感真實性：消費者對人工智慧生成音樂的反應與行銷策略意涵
Emotional Authenticity in the Age of Artificial Creativity: Consumer Responses to AI-Generated Music and Implications for Marketing Strategy
指導教授： 何富年
Ho, Foo Nin
口試委員: 冷則剛
Leng, Tse Kang
蘇威傑
Su, Weichieh
學位類別： 碩士
Master
系所名稱： 商學院 - 國際經營管理英語碩士學位學程(IMBA)
International MBA Program College of Commerce(IMBA)
論文出版年： 2026
畢業學年度： 114
語文別： 英文
論文頁數： 65
中文關鍵詞： AI生成音樂 、感知真實性 、情感投入 、AI著作 、行銷策略
外文關鍵詞： AI-generated music, perceived authenticity, emotional engagement, AI authorship, marketing strategy
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    • 本論文探討感知 AI 創作來源如何影響消費者對音樂的反應，以及其對行銷的意涵。隨著生成式人工智慧在行銷內容中日益普及，品牌面臨效率與真實性之間的權衡。本研究檢視標示為 AI 生成的音樂是否會比標示為人類創作的音樂產生較低的情感投入，並進一步探討感知真實性是否能解釋此一效果。

    本研究透過一項包含 140 位參與者的控制實驗發現，相較於標示為人類創作的音樂，標示為 AI 生成的音樂在情感投入與感知真實性上皆顯著較低。感知真實性在此關係中具有部分中介效果，而揭露時機與對 AI 的一般態度則未呈現顯著的調節效果。

    根據上述發現，本論文對 AI 生成音樂的使用提出審慎的行銷意涵。研究結果顯示，AI 生成音樂不應僅被視為提升生產效率的工具，尤其是在強調情感表達的溝通情境中更是如此。創作來源可能成為消費者可感知的線索，進而影響感知真實性與情感投入。然而，這些意涵僅限於消費者對音樂的反應，不應被解讀為廣泛適用於品牌策略的整體建議。

    This thesis examines how perceived AI authorship affects consumer responses to music and its implications for marketing. As generative AI becomes increasingly common in marketing content, brands face a trade-off between efficiency and authenticity. This study investigates whether music labeled as AI-generated produces lower emotional engagement than music labeled as human-created, and whether perceived authenticity explains this effect.
    A controlled experiment with 140 participants found that music labeled as AI-generated resulted in significantly lower emotional engagement and perceived authenticity than music labeled as human-created. Perceived authenticity partially mediated this relationship, while disclosure timing and general attitudes toward AI showed no significant moderating effects.
    Based on these findings, this thesis offers cautious marketing implications for the use of AI-generated music. The results suggest that AI-generated music should not be evaluated only as a production-efficiency tool, especially in emotionally expressive communication contexts. Authorship can function as a consumer-facing cue that shapes perceived authenticity and emotional engagement. These implications are limited to consumer responses to music and should not be read as broad branding prescriptions.

    1. Introduction 1
    1.1. Background: The Rise of AI-Generated Creative Content 1
    1.2. AI Music and the Commercialization of Artificial Creativity 2
    1.3. The Marketing Strategy Tension: Efficiency vs. Authenticity 3
    1.4. Music as a Bridge Between Brands and Consumer Emotion 5
    1.5. Research Problem: The Authenticity Risk of AI-Generated Music 5
    1.6. Research Objectives and Research Questions 6
    1.7. Research Contribution and Marketing Relevance 7
    1.8. Thesis Structure 8
    2. Literature Review 9
    2.1. Introduction 9
    2.2. Algorithm Aversion in Human vs. AI Decision Contexts 9
    2.2.1. Algorithm Aversion and Human Preference 9
    2.2.2. 1.2 Sensitivity to Algorithmic Errors 9
    2.2.3. The Role of Control in Reducing Aversion 10
    2.2.4. Task Subjectivity and Algorithm Trust 10
    2.3. Perceived Authenticity in Creative Content 11
    2.3.1. Authenticity as a Consumer Perception 11
    2.3.2. The Role of Authorship in Creative Evaluation 11
    2.3.3. AI Authorship and Perceived Authenticity 12
    2.3.4. Authenticity as a Driver of Consumer Response 12
    2.4. Disclosure Timing and Expectation Effects 13
    2.4.1. Expectations Shape Experience 13
    2.4.2. Framing and Timing of Information 13
    2.4.3. Implications for AI-Generated Music 13
    2.5. Music as Hedonic and Experiential Consumption 14
    2.5.1. Music as a Hedonic Experience 14
    2.5.2. Subjective Evaluation of Music 14
    2.5.3. Implications for Enjoyment and Emotional Engagement 14
    2.6. Research Gap 15
    2.6.1. Integration of Existing Literature 15
    2.6.2. Unaddressed Gaps 15
    2.6.3. Contribution of the Present Study 16
    3. Conceptual Framework & Hypotheses 17
    3.1. Conceptual Model 17
    3.2. Hypotheses Development 18
    3.2.1. AI Authorship and Emotional Engagement 18
    3.2.2. AI Authorship and Perceived Authenticity 19
    3.2.3. The Mediating Role of Perceived Authenticity 20
    3.2.4. The Role of Disclosure Timing 21
    3.2.5. Moderating Role of Attitudes Toward AI 22
    4. Methodology 23
    4.1. Research Design 23
    4.2. Experimental Design and Stimuli 23
    4.3. Sample and Data Collection 24
    4.4. Measures 26
    4.4.1. Emotional Engagement 26
    4.4.2. Perceived Authenticity 27
    4.4.3. Enjoyment 27
    4.4.4. Attitudes Toward AI 27
    4.4.5. Manipulation Checks 28
    4.5. Procedure 28
    4.6. Analytical Strategy 29
    4.6.1. Main Effects (H1 and H2) 29
    4.6.2. Mediation Analysis (H3) 29
    4.6.3. Moderation Analysis (H4 and H5) 30
    4.6.4. Moderated Mediation Analysis 31
    5. Results and Analysis 32
    5.1. Sample Overview 32
    5.2. Descriptive Statistics 32
    5.3. Reliability Assessment 33
    5.4. Descriptive Statistics 34
    5.5. Hypothesis Testing 36
    5.5.1. Hypothesis 1: Effect of AI Authorship on Emotional Engagement 36
    5.5.2. Hypothesis 2: Effect of AI Authorship on Perceived Authenticity 37
    5.5.3. Hypothesis 3: Mediating Role of Perceived Authenticity 38
    5.5.4. Hypothesis 4: Moderating Effect of Disclosure Timing 40
    5.5.5. Hypothesis 5: Moderating Effect of Attitudes Toward AI 42
    5.5.6. Summary of Hypothesis Testing Results 45
    6. Discussion and Marketing Communication Implications 46
    6.1. Interpretation of the Main Findings 46
    6.1.1. AI authorship and emotional engagement 46
    6.1.2. Implications for marketing communication 46
    6.1.3. AI authorship, perceived authenticity, and emotional engagement 46
    6.1.4. What the non-supported hypotheses suggest 47
    6.2. Theoretical Implications 48
    6.2.1. Extending algorithm aversion into creative consumption 48
    6.2.2. Positioning authorship as an authenticity cue 49
    6.2.3. Connecting hedonic experience with source interpretation 49
    6.2.4. The limited role of disclosure timing and AI attitudes 49
    6.3. Marketing Communication Implications 50
    6.3.1. The managerial challenge: efficiency versus authenticity 50
    6.3.2. The role of music in the communication context 51
    6.3.3. Higher-risk contexts: emotionally symbolic campaigns 51
    6.3.4. Coca-Cola as an illustrative example 51
    6.4. Disclosure Strategy 52
    6.4.1. Disclosure as a salience decision 52
    6.4.2. When AI authorship may not need to be foregrounded 52
    6.4.3. When explicit disclosure is more important 53
    6.4.4. Strategic principle for disclosure 53
    6.5. Practical Considerations for Marketers 54
    6.6. Chapter Summary 56
    7. Conclusion 57
    7.1. Summary of Key Findings 57
    7.2. Overall Contribution 57
    7.3. Limitations and Future Research 57
    7.4. Final Conclusion 59
    Reference 60
    Appendix 63
    Music Listening Experience Survey 63

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