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題名 生成式人工智慧時代的行銷：消費者偏好人工撰寫還是人工智慧生成內容？
Marketing in the GenAI era: Do consumers prefer human or AI-generated content?作者 李山林
Lei, San-Lam貢獻者 朴星俊
Park, Sung-Jun
李山林
Lei, San-Lam關鍵詞 AI生成內容
機器啟發式
感知獨特性
感知價值
支付意願
AI-generated content
Machine heuristics
Perceived uniqueness
Perceived value
Willingness to pay日期 2025 上傳時間 2-Jun-2025 14:38:13 (UTC+8) 摘要 消費者對由人工智慧（AI）撰寫的行銷內容與人類撰寫的內容是否有不同的反應？本研究探討AI在行銷內容創作中的有效性，特別聚焦於生成式AI的應用。以「機器啟發式」（machine heuristics）為理論基礎，本研究分析兩個關鍵因素——感知獨特性與感知價值——如何影響消費者對不同來源（人類 vs. AI）撰寫的行銷內容的認知。實驗結果（n = 411）顯示，消費者對人類撰寫的行銷內容反應較為正面，對其所推廣的產品展現出較高的支付意願，相較之下，AI撰寫的內容效果較弱。中介分析進一步指出，AI生成的內容會降低感知獨特性，進而降低感知價值，最終導致支付意願下降。透過探討這些影響消費者認知的潛在機制，本研究將機器啟發式的概念延伸至AI生成行銷內容的領域，強調感知獨特性與感知價值在塑造消費者反應中的重要角色，同時揭示AI在模仿人類創意行銷溝通上的挑戰。本研究有助於釐清人類與AI協作於行銷內容創作中應達到的最佳平衡點。
Do consumers response differently to marketing content written by AI compared to human counterparts? This study investigates the efficacy of AI in content creation of marketing, with a particular focus on generative AI. Grounded the concept of machine heuristics, this study explores the key factors—perceived uniqueness and perceived value—that underlie the consumer perceptions of marketing content written by different writer sources (human vs. AI). The results (n = 411) demonstrate that consumers exhibit a more favorable response to marketing content written by humans, showing a higher willingness to pay for products advertized by human-generated marketing content as oppose to AI-generated content. Mediation analysis further indicates that AI-generated content leads to a lower perceived uniqueness, which subsequently reduces perceived value, and consequently resulting in a decreased willingness to pay. By investigating these underlying mechanism in consumer perception, this study extends the framework of machine heuristics to the domain of AI-generated marketing content. It highlights the significant role of perceived uniqueness and perceived value in shaping consumer responses to marketing content, emphasizing the challenges AI encounters in replicating human creativity in marketing communication. The findings contribute to identifying the optimal balance in human-AI collaboration for marketing content creation.參考文獻 Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427-445. Akdim, K., & Casaló, L. V. (2023). Perceived value of AI-based recommendations service: The case of voice assistants. Service Business, 17(1), 81-112. Banks, J., Edwards, A. P., & Westerman, D. (2021). The space between: Nature and machine heuristics in evaluations of organisms, cyborgs, and robots. Cyberpsychology, Behavior, and Social Networking, 24(5), 324-331. Cameron, T. A., & James, M. D. (1987). Estimating willingness to pay from survey data: An alternative pre-test-market evaluation procedure. Journal of Marketing Research, 24(November), 389–395. Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Houghton Mifflin Co. De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K. U., & Von Wangenheim, F. (2020). Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51(1), 91-105. De Cremer, D., Bianzino, N. M., & Falk, B. (2023). How generative AI could disrupt creative work. Harvard Business Review, 13. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. Foo, L. G., Rahmani, H., & Liu, J. (2023). Ai-generated content (aigc) for various data modalities: A survey. arXiv preprint arXiv:2308.14177. Franke, N., & Schreier, M. (2008). Product uniqueness as a driver of customer utility in mass customization. Marketing Letters, 19, 93-107. Gallarza, M. G., Maubisson, L., & Riviere, A. (2021). Replicating consumer value scales: A comparative study of EVS and PERVAL at a cultural heritage site. Journal of Business Research, 126, 614-623. Getty Images (2024). Nearly 90% of consumers want transparency on AI images finds Getty Images report. https://newsroom.gettyimages.com/en/getty-images/nearly-90-of-consumers-want-transparency-on-ai-images-finds-getty-images-report Gourville, J. T. (2006). Eager sellers and stony buyers: Understanding the psychology of new-product adoption. Harvard Business Review, 84(6), 98-106. Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162, 120392. Haenlein, M., Anadol, E., Farnsworth, T., Hugo, H., Hunichen, J., & Welte, D. (2020). Navigating the new era of influencer marketing: How to be successful on Instagram, TikTok, & Co. California Management Review, 63(1), 5-25. Haslam, N. (2006). Dehumanization: An integrative review. Personality and Social Psychology Review, 10(3), 252–264. https://doi.org/10.1207/s15327957pspr1003_4 Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). Guilford Press. Hollebeek, L. D., & Macky, K. (2019). Digital content marketing's role in fostering consumer engagement, trust, and value: Framework, fundamental propositions, and implications. Journal of Interactive Marketing, 45(1), 27-41. Holliman, G., & Rowley, J. (2014). 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國立政治大學
國際經營管理英語碩士學位學程(IMBA)
112933027資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112933027 資料類型 thesis dc.contributor.advisor 朴星俊 zh_TW dc.contributor.advisor Park, Sung-Jun en_US dc.contributor.author (Authors) 李山林 zh_TW dc.contributor.author (Authors) Lei, San-Lam en_US dc.creator (作者) 李山林 zh_TW dc.creator (作者) Lei, San-Lam en_US dc.date (日期) 2025 en_US dc.date.accessioned 2-Jun-2025 14:38:13 (UTC+8) - dc.date.available 2-Jun-2025 14:38:13 (UTC+8) - dc.date.issued (上傳時間) 2-Jun-2025 14:38:13 (UTC+8) - dc.identifier (Other Identifiers) G0112933027 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/157212 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 國際經營管理英語碩士學位學程(IMBA) zh_TW dc.description (描述) 112933027 zh_TW dc.description.abstract (摘要) 消費者對由人工智慧（AI）撰寫的行銷內容與人類撰寫的內容是否有不同的反應？本研究探討AI在行銷內容創作中的有效性，特別聚焦於生成式AI的應用。以「機器啟發式」（machine heuristics）為理論基礎，本研究分析兩個關鍵因素——感知獨特性與感知價值——如何影響消費者對不同來源（人類 vs. AI）撰寫的行銷內容的認知。實驗結果（n = 411）顯示，消費者對人類撰寫的行銷內容反應較為正面，對其所推廣的產品展現出較高的支付意願，相較之下，AI撰寫的內容效果較弱。中介分析進一步指出，AI生成的內容會降低感知獨特性，進而降低感知價值，最終導致支付意願下降。透過探討這些影響消費者認知的潛在機制，本研究將機器啟發式的概念延伸至AI生成行銷內容的領域，強調感知獨特性與感知價值在塑造消費者反應中的重要角色，同時揭示AI在模仿人類創意行銷溝通上的挑戰。本研究有助於釐清人類與AI協作於行銷內容創作中應達到的最佳平衡點。 zh_TW dc.description.abstract (摘要) Do consumers response differently to marketing content written by AI compared to human counterparts? This study investigates the efficacy of AI in content creation of marketing, with a particular focus on generative AI. Grounded the concept of machine heuristics, this study explores the key factors—perceived uniqueness and perceived value—that underlie the consumer perceptions of marketing content written by different writer sources (human vs. AI). The results (n = 411) demonstrate that consumers exhibit a more favorable response to marketing content written by humans, showing a higher willingness to pay for products advertized by human-generated marketing content as oppose to AI-generated content. Mediation analysis further indicates that AI-generated content leads to a lower perceived uniqueness, which subsequently reduces perceived value, and consequently resulting in a decreased willingness to pay. By investigating these underlying mechanism in consumer perception, this study extends the framework of machine heuristics to the domain of AI-generated marketing content. It highlights the significant role of perceived uniqueness and perceived value in shaping consumer responses to marketing content, emphasizing the challenges AI encounters in replicating human creativity in marketing communication. The findings contribute to identifying the optimal balance in human-AI collaboration for marketing content creation. en_US dc.description.tableofcontents Acknowledgements i Abstract ii Table of Contents iii List of Figures and Tables v 1. Introduction 1 2. Literature Review 4 2.1. Content Creation by Human and AI 4 2.1.1. Content Marketing and Artificial Intelligence 4 2.1.2. Machine Heuristic 5 2.2. Willingness to pay 7 2.3. Perceived Uniqueness and Perceived Value 8 3. Methodology 11 3.1. Data Collection 11 3.2. Survey Design 11 3.3. Measurement Instruments 11 3.4. Analysis 12 4. Results 13 4.1. Demographic Profiles 13 4.2. Manipulation Check 13 4.3. Willingness to Pay 14 4.4. Mediation Effect Analysis 15 5. General Discussion 16 5.1. Overview of Findings 16 5.2. Theoretical Implications 17 5.3. Managerial Implications 18 5.4. Limitations and Future Research 19 References 22 Appendix 1. Instagram Advertizement 29 Appendix 2. Measurement Items 30 zh_TW dc.format.extent 3438840 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112933027 en_US dc.subject (關鍵詞) AI生成內容 zh_TW dc.subject (關鍵詞) 機器啟發式 zh_TW dc.subject (關鍵詞) 感知獨特性 zh_TW dc.subject (關鍵詞) 感知價值 zh_TW dc.subject (關鍵詞) 支付意願 zh_TW dc.subject (關鍵詞) AI-generated content en_US dc.subject (關鍵詞) Machine heuristics en_US dc.subject (關鍵詞) Perceived uniqueness en_US dc.subject (關鍵詞) Perceived value en_US dc.subject (關鍵詞) Willingness to pay en_US dc.title (題名) 生成式人工智慧時代的行銷：消費者偏好人工撰寫還是人工智慧生成內容？ zh_TW dc.title (題名) Marketing in the GenAI era: Do consumers prefer human or AI-generated content? en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427-445. Akdim, K., & Casaló, L. V. (2023). Perceived value of AI-based recommendations service: The case of voice assistants. Service Business, 17(1), 81-112. Banks, J., Edwards, A. P., & Westerman, D. (2021). The space between: Nature and machine heuristics in evaluations of organisms, cyborgs, and robots. Cyberpsychology, Behavior, and Social Networking, 24(5), 324-331. Cameron, T. A., & James, M. D. (1987). Estimating willingness to pay from survey data: An alternative pre-test-market evaluation procedure. Journal of Marketing Research, 24(November), 389–395. Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Houghton Mifflin Co. De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K. U., & Von Wangenheim, F. (2020). Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51(1), 91-105. De Cremer, D., Bianzino, N. M., & Falk, B. (2023). How generative AI could disrupt creative work. Harvard Business Review, 13. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. Foo, L. G., Rahmani, H., & Liu, J. (2023). Ai-generated content (aigc) for various data modalities: A survey. arXiv preprint arXiv:2308.14177. Franke, N., & Schreier, M. (2008). Product uniqueness as a driver of customer utility in mass customization. Marketing Letters, 19, 93-107. Gallarza, M. G., Maubisson, L., & Riviere, A. (2021). Replicating consumer value scales: A comparative study of EVS and PERVAL at a cultural heritage site. Journal of Business Research, 126, 614-623. Getty Images (2024). Nearly 90% of consumers want transparency on AI images finds Getty Images report. https://newsroom.gettyimages.com/en/getty-images/nearly-90-of-consumers-want-transparency-on-ai-images-finds-getty-images-report Gourville, J. T. (2006). Eager sellers and stony buyers: Understanding the psychology of new-product adoption. Harvard Business Review, 84(6), 98-106. Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162, 120392. Haenlein, M., Anadol, E., Farnsworth, T., Hugo, H., Hunichen, J., & Welte, D. (2020). Navigating the new era of influencer marketing: How to be successful on Instagram, TikTok, & Co. California Management Review, 63(1), 5-25. Haslam, N. (2006). Dehumanization: An integrative review. Personality and Social Psychology Review, 10(3), 252–264. https://doi.org/10.1207/s15327957pspr1003_4 Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). Guilford Press. Hollebeek, L. D., & Macky, K. (2019). Digital content marketing's role in fostering consumer engagement, trust, and value: Framework, fundamental propositions, and implications. Journal of Interactive Marketing, 45(1), 27-41. Holliman, G., & Rowley, J. (2014). Business to business digital content marketing: Marketers’ perceptions of best practice. Journal of Research in Interactive Marketing, 8(4), 269-293. Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30-50. Järvinen, J., & Taiminen, H. (2016). Harnessing marketing automation for B2B content marketing. Industrial Marketing Management, 54, 164-175. Kallel, A., Ben Dahmane Mouelhi, N., Chaouali, W., & Danks, N. P. (2024). Hey chatbot, why do you treat me like other people? 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