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題名 《主播型態對觀眾接受度及沉浸感的影響：真實主播、虛擬分身與全新虛擬角色之比較研究》
Real vs. Avatar vs. Fictional Anchors: Effects on Audience Acceptance and Immersion
作者 李家妤
Lee, Chia-Yu
貢獻者 張君豪
Tim Chang
李家妤
Lee, Chia-Yu
關鍵詞 人工智慧主播
沉浸感
觀眾接受度
新聞可信度
虛擬主播
RAF 架構
AI news anchors
Immersion
Audience acceptance
Credibility
RAF framework
日期 2026
上傳時間 2-Mar-2026 12:38:26 (UTC+8)
摘要 隨著人工智慧技術快速發展，AI 生成新聞主播逐漸進入各國新聞媒體實務場域，包括中國、日本、新加坡與臺灣。然而，觀眾是否真正接受這些非真人主播，其觀看體驗、信任感與情感連結如何形成，仍缺乏系統性的實證研究。特別是既有研究多將「虛擬主播」視為單一類型，忽略不同虛擬呈現形式可能引發截然不同的心理反應。 本研究以新聞傳播與人機互動理論為基礎，提出 RAF（Real–Avatar–Fictional）主播分類架構，區分三種新聞主播類型：真人主播（Real Anchor）、擬真人虛擬主播（Avatar Anchor）與虛構型 AI 主播（Fictional Anchor），探討其對觀眾沉浸感與接受度之影響，並進一步檢驗沉浸感是否在主播類型與接受度之間扮演中介角色。 研究採用混合方法設計，透過實驗方式讓受試者觀看三種主播版本的新聞內容，並控制腳本、畫面與節奏一致，以排除內容干擾。量化部分共蒐集 210 份有效問卷，測量沉浸感與接受度四構面（可信度、專業性、情感親近感與忠誠度），並進行單因子變異數分析與中介效果分析；質化部分則透過開放式問答與訪談，補充觀眾對不同主播的主觀感受與解釋。 研究結果顯示，真人主播在沉浸感與整體接受度上皆顯著高於 Avatar 與 Fictional 主播；Avatar 主播呈現中介水準，其高度擬真人外觀同時引發科技感與情感落差；Fictional 主播則在娛樂性與新奇感上具吸引力，但在可信度與專業性評價上最低。中介分析結果進一步指出，沉浸感在主播類型與接受度之間具有部分中介效果，顯示沉浸感為影響觀眾是否接受 AI 主播的重要心理機制。 本研究在理論上驗證 RAF 架構的分析效力，並將沉浸理論延伸至 AI 新聞主播情境；在實務上則建議新聞機構採取「真人主導、AI 輔助」的共存式播報策略，並重視虛擬主播的情感設計與沉浸體驗，以提升觀眾信任與接受度。
With the rapid advancement of artificial intelligence technologies, AI-generated news anchors have increasingly entered newsroom practices across countries such as China, Japan, Singapore, and Taiwan. Despite their growing presence, it remains unclear whether audiences truly accept these non-human anchors, how immersed they feel while watching them, and how trust and emotional connection are formed. Moreover, existing studies often treat “virtual anchors” as a single category, overlooking the psychological differences generated by varying degrees of realism and stylization. To address this gap, this study proposes the RAF framework, which distinguishes among Real, Avatar, and Fictional news anchors. Drawing on theories of immersion, anthropomorphism, source credibility, and human–AI communication, this study examines how different anchor types influence audience immersion and acceptance, and whether immersion mediates the relationship between anchor type and acceptance. A mixed-methods experimental design was employed. Participants viewed news videos presented by one of the three anchor types, with identical scripts, visuals, and pacing to control for content effects. Quantitative data were collected from 210 participants using an online questionnaire measuring immersion and four dimensions of acceptance: credibility, professionalism, emotional closeness, and loyalty. One-way ANOVA and mediation analyses were conducted. Qualitative data from open-ended responses and interviews were analyzed to provide deeper insights into audience perceptions. The results indicate that real anchors generate the highest levels of immersion and acceptance, followed by avatar anchors, while fictional anchors receive the lowest scores in credibility and professionalism. Avatar anchors evoke ambivalent responses, combining perceptions of technological sophistication with emotional discomfort. Fictional anchors are often perceived as interesting or entertaining but lack perceived trustworthiness. Mediation analysis further reveals that immersion plays a partial mediating role between anchor type and audience acceptance, highlighting immersion as a key psychological mechanism shaping audience evaluations. Theoretically, this study validates the RAF framework and extends immersion theory to AI-mediated news contexts. Practically, the findings suggest that AI anchors should 1 not replace human anchors but function as supportive or complementary agents. Emphasizing emotional design and immersive experience is crucial for the successful integration of AI anchors in journalism.
參考文獻 Breviglieri, F., Guerreiro, J. M., & Loureiro, S. (2025). Artificial intelligence versus human news anchors: Trust in the age of AI. Journal of Marketing Communications, 1–27. https://doi.org/10.1080/13527266.2025.2573320 Chen, F., & Guo, T. (2021). Effects of competence information on perceptions of warmth. Asian Journal of Social Psychology, 24(4), 524–536. https://doi.org/10.1111/ajsp.12452 De la Peña, N., Weil, P., Llobera, J., Giannopoulos, E., Pomés, A., Spanlang, B., & Slater, M. (2010). Immersive journalism: Immersive virtual reality for the first-person experience of news. Presence: Teleoperators and Virtual Environments, 19(4), 291–301. https://doi.org/10.1162/PRES_a_00005 Deuze, M. (2005). What is journalism? Professional identity and ideology of journalists reconsidered. Journalism, 6(4), 442–464. https://doi.org/10.1177/1464884905056815 Gaziano, C., & McGrath, K. (1986). Measuring the concept of credibility. Journalism Quarterly, 63(3), 451–462. https://doi.org/10.1177/107769908606300301 Gao, Y., Dai, Y., Zhang, G., Guo, H., Mostajeran, F., & Zheng, B. (2025). Trust in virtual agents: Exploring the role of stylization and voice. IEEE Transactions on Visualization and Computer Graphics, 31(5), 3623–3633. https://doi.org/10.1109/TVCG.2025.3549566 Hartmann, T. (2016). Mass communication and para-social interaction. In Schlüsselwerke der Medienwirkungsforschung (pp. 75–84). Springer. https://doi.org/10.1007/978-3-658-09923-7_7 Henestrosa, A. L. (2023). Automated journalism: The effects of AI authorship and evaluative information on the perception of a science journalism article. Computers in Human Behavior, 138, 107445. https://doi.org/10.1016/j.chb.2022.107445 Horton, D., & Wohl, R. R. (1956). Mass communication and parasocial interaction: Observations on intimacy at a distance. Psychiatry, 19(3), 215–229. https://doi.org/10.1080/00332747.1956.11023049 Kätsyri, J., Förger, K., Mäkäräinen, M., & Takala, T. (2015). A review of empirical evidence on different uncanny valley hypotheses. Frontiers in Psychology, 6, 390. https://doi.org/10.3389/fpsyg.2015.00390 Kiousis, S. (2001). Public trust or mistrust? Perceptions of media credibility in the information age. Mass Communication & Society, 4(4), 381–403. https://doi.org/10.1207/S15327825MCS0404_4 Labrecque, L. I. (2014). Fostering consumer–brand relationships in social media environments. Journal of Interactive Marketing, 28(2), 134–148. https://doi.org/10.1016/j.intmar.2013.12.003 Lee, K. M. (2004). Presence, explicated. Communication Theory, 14(1), 27–50. https://doi.org/10.1111/j.1468-2885.2004.tb00302.x Lee, K. M., & Watkins, B. (2016). YouTube vloggers’ influence on consumer luxury brand perceptions. Journal of Business Research, 69(12), 5753–5760. https://doi.org/10.1016/j.jbusres.2016.04.171 Lombard, M., & Ditton, T. (1997). At the heart of it all: The concept of presence. Journal of Computer-Mediated Communication, 3(2). https://doi.org/10.1111/j.1083-6101.1997.tb00072.x Lombard, M., & Xu, K. (2021). Social responses to media technologies. Human-Machine Communication, 2(1), 29–55. https://doi.org/10.30658/hmc.2.2 Mori, M., MacDorman, K. F., & Kageki, N. (2012). The uncanny valley. IEEE Robotics & Automation Magazine, 19(2), 98–100. https://doi.org/10.1109/MRA.2012.2192811 Nowak, K. L., & Biocca, F. (2003). The effect of agency and anthropomorphism. Presence, 12(5), 481–494. https://doi.org/10.1162/105474603322761289 Nowak, K. L., & Fox, J. (2018). Avatars and computer-mediated communication. Review of Communication Research, 6, 30–53. https://doi.org/10.12840/issn.2255-4165.2018.06.01.015 Opdahl, A. L. (2023). Trustworthy journalism through AI. Data & Knowledge Engineering, 146, 102182. https://doi.org/10.1016/j.datak.2023.102182 Park, J., Oh, C., & Kim, H. Y. (2024). AI vs. human-generated content on Instagram. Technology in Society, 79, 102705. https://doi.org/10.1016/j.techsoc.2024.102705 Qiu, L., & Benbasat, I. (2009). Evaluating anthropomorphic product recommendation agents. Journal of Management Information Systems, 25(4), 145–181. Reeves, B., & Nass, C. (1996). The media equation. Cambridge University Press. Shin, D. (2018). Empathy and embodied experience in virtual environments. Computers in Human Behavior, 78, 64–73. https://doi.org/10.1016/j.chb.2017.09.012 Shin, D., & Biocca, F. (2018). Exploring immersive experience in journalism. New Media & Society, 19(11), 1795–1816. https://doi.org/10.1177/1461444817733133 Slater, M., & Wilbur, S. (1997). A framework for immersive virtual environments. Presence, 6, 603–616. Sundar, S. S., Oh, J., & Jia, H. (2021). Warmth in machines. Computers in Human Behavior, 120, 106757. https://doi.org/10.1016/j.chb.2021.106757 Thomson, T. J., Thomas, R. J., Riedlinger, M., & Matich, P. (2025). Generative AI & journalism. https://doi.org/10.6084/m9.figshare.28068008 Thurman, N., Dörr, K. N., & Kunert, J. (2017). When reporters get hands-on with robo-writing. Digital Journalism, 5(10), 1240–1259. https://doi.org/10.1080/21670811.2017.1289819 Thurman, N., Dörr, K. N., & Kunert, J. (2022). When reporters get hands-on with robo-journalism. Digital Journalism, 10(6), 943–964. https://doi.org/10.1080/21670811.2022.2037379 Vorderer, P., Wirth, W., Saari, T., & Gouveia, F. R. (2003). Constructing presence. In G. Riva et al. (Eds.), Being there (pp. 225–240). IOS Press. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. Alnasser, A., Williams, B., & Gosling, C. M. (2025). How is professionalism measured in health care professions? Health Sciences Review, 10, 100224. https://doi.org/10.1016/j.hsr.2025.100224 Hua, A. (2025). Comparative study on the credibility of AI-generated news content and traditional news content. https://doi.org/10.70267/vs7xzc44 Fitria, T. N. (2024). Artificial intelligence (AI) news anchors: How do they perform in the journalistic sector? iTELL-ArtificialIntelligenceAINewsAnchors.pdf Hill, S. R., & Troshani, I. (2024). Chatbot anthropomorphism, social presence, uncanniness and brand attitude effects. Journal of Computer Information Systems. https://doi.org/10.1080/08874417.2024.2423187
描述 碩士
國立政治大學
全球傳播與創新科技碩士學位學程
112ZM1023
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112ZM1023
資料類型 thesis
dc.contributor.advisor 張君豪zh_TW
dc.contributor.advisor Tim Changen_US
dc.contributor.author (Authors) 李家妤zh_TW
dc.contributor.author (Authors) Lee, Chia-Yuen_US
dc.creator (作者) 李家妤zh_TW
dc.creator (作者) Lee, Chia-Yuen_US
dc.date (日期) 2026en_US
dc.date.accessioned 2-Mar-2026 12:38:26 (UTC+8)-
dc.date.available 2-Mar-2026 12:38:26 (UTC+8)-
dc.date.issued (上傳時間) 2-Mar-2026 12:38:26 (UTC+8)-
dc.identifier (Other Identifiers) G0112ZM1023en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/161884-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 全球傳播與創新科技碩士學位學程zh_TW
dc.description (描述) 112ZM1023zh_TW
dc.description.abstract (摘要) 隨著人工智慧技術快速發展，AI 生成新聞主播逐漸進入各國新聞媒體實務場域，包括中國、日本、新加坡與臺灣。然而，觀眾是否真正接受這些非真人主播，其觀看體驗、信任感與情感連結如何形成，仍缺乏系統性的實證研究。特別是既有研究多將「虛擬主播」視為單一類型，忽略不同虛擬呈現形式可能引發截然不同的心理反應。 本研究以新聞傳播與人機互動理論為基礎，提出 RAF（Real–Avatar–Fictional）主播分類架構，區分三種新聞主播類型：真人主播（Real Anchor）、擬真人虛擬主播（Avatar Anchor）與虛構型 AI 主播（Fictional Anchor），探討其對觀眾沉浸感與接受度之影響，並進一步檢驗沉浸感是否在主播類型與接受度之間扮演中介角色。 研究採用混合方法設計，透過實驗方式讓受試者觀看三種主播版本的新聞內容，並控制腳本、畫面與節奏一致，以排除內容干擾。量化部分共蒐集 210 份有效問卷，測量沉浸感與接受度四構面（可信度、專業性、情感親近感與忠誠度），並進行單因子變異數分析與中介效果分析；質化部分則透過開放式問答與訪談，補充觀眾對不同主播的主觀感受與解釋。 研究結果顯示，真人主播在沉浸感與整體接受度上皆顯著高於 Avatar 與 Fictional 主播；Avatar 主播呈現中介水準，其高度擬真人外觀同時引發科技感與情感落差；Fictional 主播則在娛樂性與新奇感上具吸引力，但在可信度與專業性評價上最低。中介分析結果進一步指出，沉浸感在主播類型與接受度之間具有部分中介效果，顯示沉浸感為影響觀眾是否接受 AI 主播的重要心理機制。 本研究在理論上驗證 RAF 架構的分析效力，並將沉浸理論延伸至 AI 新聞主播情境；在實務上則建議新聞機構採取「真人主導、AI 輔助」的共存式播報策略，並重視虛擬主播的情感設計與沉浸體驗，以提升觀眾信任與接受度。zh_TW
dc.description.abstract (摘要) With the rapid advancement of artificial intelligence technologies, AI-generated news anchors have increasingly entered newsroom practices across countries such as China, Japan, Singapore, and Taiwan. Despite their growing presence, it remains unclear whether audiences truly accept these non-human anchors, how immersed they feel while watching them, and how trust and emotional connection are formed. Moreover, existing studies often treat “virtual anchors” as a single category, overlooking the psychological differences generated by varying degrees of realism and stylization. To address this gap, this study proposes the RAF framework, which distinguishes among Real, Avatar, and Fictional news anchors. Drawing on theories of immersion, anthropomorphism, source credibility, and human–AI communication, this study examines how different anchor types influence audience immersion and acceptance, and whether immersion mediates the relationship between anchor type and acceptance. A mixed-methods experimental design was employed. Participants viewed news videos presented by one of the three anchor types, with identical scripts, visuals, and pacing to control for content effects. Quantitative data were collected from 210 participants using an online questionnaire measuring immersion and four dimensions of acceptance: credibility, professionalism, emotional closeness, and loyalty. One-way ANOVA and mediation analyses were conducted. Qualitative data from open-ended responses and interviews were analyzed to provide deeper insights into audience perceptions. The results indicate that real anchors generate the highest levels of immersion and acceptance, followed by avatar anchors, while fictional anchors receive the lowest scores in credibility and professionalism. Avatar anchors evoke ambivalent responses, combining perceptions of technological sophistication with emotional discomfort. Fictional anchors are often perceived as interesting or entertaining but lack perceived trustworthiness. Mediation analysis further reveals that immersion plays a partial mediating role between anchor type and audience acceptance, highlighting immersion as a key psychological mechanism shaping audience evaluations. Theoretically, this study validates the RAF framework and extends immersion theory to AI-mediated news contexts. Practically, the findings suggest that AI anchors should 1 not replace human anchors but function as supportive or complementary agents. Emphasizing emotional design and immersive experience is crucial for the successful integration of AI anchors in journalism.en_US
dc.description.tableofcontents Chapter 1 Introduction 8 Research Objectives 13 Research Questions 13 Thesis Structure 14 Chapter 2 Theoretical Background 15 2.1 Immersion Theory 15 2.2 Anthropomorphism and Virtual Characters 16 2.3 News Credibility and Acceptance 18 2.4 Interactive News 19 Chapter 3 Methodology 21 3.1 Research Design: The RAF Model 21 3.2 Participants 28 3.3 Research Instruments 29 3.3.1 Survey Scales (5-point Likert) 30 3.4 Social Audience Analysis 31 3.5 Viewing Conditions and Perceived Interactivity 32 3.6 Virtual Anchor Implementation and Production Control 33 3.6.1 Tools 33 3.6.2 Consistency Controls 34 3.7 Data Analysis 34 Quantitative Analysis 34 Qualitative Analysis 35 Chapter 4 Results 36 4.1 Sample Characteristics and Descriptive Statistics 36 4.1.1 Sample Overview 36 4.1.2 Demographic Characteristics 37 Residence 37 Gender 37 Age 37 Education Level 38 4.1.3 News Consumption Habits 38 News Viewing Frequency 38 Primary News Platforms 39 4.1.4 Descriptive Statistics of Key Variables 39 4.1.5 Section Summary 40 4.2 Analytical Framework and Variable Definitions 40 4.2.1 Anchor Type Classification (RAF Framework) 41 4.2.2 Dependent Variables: Immersion and Acceptance 41 4.2.3 Moderating Variables and Group Comparisons 42 4.2.4 Analytical Strategy 43 4.2.5 Section Summary 43 4.3 Quantitative Results 44 4.3.1 Effects of Anchor Type on Immersion 44 4.3.2 Effects of Anchor Type on Acceptance 46 4.3.3 The One-way ANOVA Results 47 4.3.4 Mediation Analysis: Immersion as Mediator 48 4.3 Section Summary 49 4.4 Cross-group Analysis 50 4.4.1 Age Group Differences in Anchor Evaluation 50 4.4.2 AI Familiarity and Acceptance of Virtual Anchors 51 4.4.3 Interaction Willingness and Acceptance Patterns 52 1. Overview 54 2. Quantitative Results 55 2.1 Reliability and Scale Validation 56 2.2 Descriptive Statistics 56 Table 1 Participant Demographics by Anchor Type 56 Table 2 Reliability Coefficients for Immersion and Acceptance Scales 57 Table 3 Comparisons of Immersion and Acceptance Across Anchor Types 58 2.3 The One-Way ANOVA Test Results 59 2.4 Mediation Analysis: Immersion → Acceptance 60 2.5 Cross-Group Observations 60 4.5 Qualitative Findings 61 4.5.1 Theme 1: Human Warmth and Emotional Authenticity 61 4.5.2 Theme 2: Emotional Disconnection and Uncanny Perception 63 4.5.3 Theme 3: Curiosity and Conditional Acceptance 64 4.5.4 Theme 4: Human–AI Coexistence 65 Summary of Findings 71 1. Purpose and Definition 72 2. Descriptive Statistics: Engagement Willingness Across Anchor Types 72 3. Thematic Coding of Open-Ended Responses 73 4. Framework of Audience Engagement 74 5. Theoretical Insights and Breakthroughs 75 6. Discussion Summary 76 4.6 Conclusion & Recommendations 77 1. Overview 77 2. Summary of Findings 77 3. Academic Contributions 79 4. Practical Implications 80 5. Limitations of the Study 81 6. Future Research Directions and Applications 82 7. Concluding Statement 84 Chapter 5 Discussion 85 5.1 Discussion of RQ1: Differences in Viewing Experience 85 5.2 Discussion of RQ2: Acceptance, Trust, and Relational Perception 87 5.3 Discussion of RQ3: Immersion as a Mediating Mechanism 88 5.4 Audience Engagement Trajectories 89 5.5 Theoretical Implications 90 5.6 Practical Implications for AI News Anchoring 90 Chapter 6 Summary 91 Conclusion and Recommendations 91 6.1 Summary of Findings 91 6.2 Academic Contributions 94 6.3 Practical Contributions 95 6.4 Limitations of the Study 97 6.5 Future Research Directions 98 6.6 Concluding Remarks 100 Reference 102zh_TW
dc.format.extent 3391129 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112ZM1023en_US
dc.subject (關鍵詞) 人工智慧主播zh_TW
dc.subject (關鍵詞) 沉浸感zh_TW
dc.subject (關鍵詞) 觀眾接受度zh_TW
dc.subject (關鍵詞) 新聞可信度zh_TW
dc.subject (關鍵詞) 虛擬主播zh_TW
dc.subject (關鍵詞) RAF 架構zh_TW
dc.subject (關鍵詞) AI news anchorsen_US
dc.subject (關鍵詞) Immersionen_US
dc.subject (關鍵詞) Audience acceptanceen_US
dc.subject (關鍵詞) Credibilityen_US
dc.subject (關鍵詞) RAF frameworken_US
dc.title (題名) 《主播型態對觀眾接受度及沉浸感的影響：真實主播、虛擬分身與全新虛擬角色之比較研究》zh_TW
dc.title (題名) Real vs. Avatar vs. Fictional Anchors: Effects on Audience Acceptance and Immersionen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Breviglieri, F., Guerreiro, J. M., & Loureiro, S. (2025). Artificial intelligence versus human news anchors: Trust in the age of AI. Journal of Marketing Communications, 1–27. https://doi.org/10.1080/13527266.2025.2573320 Chen, F., & Guo, T. (2021). Effects of competence information on perceptions of warmth. Asian Journal of Social Psychology, 24(4), 524–536. https://doi.org/10.1111/ajsp.12452 De la Peña, N., Weil, P., Llobera, J., Giannopoulos, E., Pomés, A., Spanlang, B., & Slater, M. (2010). Immersive journalism: Immersive virtual reality for the first-person experience of news. Presence: Teleoperators and Virtual Environments, 19(4), 291–301. https://doi.org/10.1162/PRES_a_00005 Deuze, M. (2005). What is journalism? Professional identity and ideology of journalists reconsidered. Journalism, 6(4), 442–464. https://doi.org/10.1177/1464884905056815 Gaziano, C., & McGrath, K. (1986). Measuring the concept of credibility. 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