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    Title: 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?
    Authors: 李山林
    Lei, San-Lam
    Contributors: 朴星俊
    Park, Sung-Jun
    李山林
    Lei, San-Lam
    Keywords: 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
    Date: 2025
    Issue Date: 2025-06-02 14:38:13 (UTC+8)
    Abstract: 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.
    Reference: 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? The role of uniqueness neglect in human-chatbot interactions. Journal of Strategic Marketing, 32(2), 170-186.  
    Kalra, A., & Goodstein, R. C. (1998). The impact of advertising positioning strategies on consumer price sensitivity. Journal of Marketing Research, 35(2), 210-224.
    Kaplan, A., & Haenlein, M. (2020). Rulers of the world, unite! The challenges and opportunities of artificial intelligence. Business Horizons, 63(1), 37-50.
    Koschate-Fischer, N., Diamantopoulos, A., & Oldenkotte, K. (2012). Are consumers really willing to pay more for a favorable country image? A study of country-of-origin effects on willingness to pay. Journal of International Marketing, 20(1), 19-41.
    Krishna, A. (1991). Effect of dealing patterns on consumer perceptions of deal frequency and willingness to pay. Journal of Marketing Research, 28(4), 441-451.
    Lee, E. J. (2024). Minding the source: Toward an integrative theory of human–machine communication. Human Communication Research, 50(2), 184-193.
    Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data & Society, 5(1), 2053951718756684.
    Liederman, E. (2025). Consumers want AI transparency from media publications. EMARKETER. https://www.emarketer.com/content/consumers-want-ai-transparency-media-publications.
    Liu, B., & Wei, L. (2018). Reading machine-written news: Effect of machine heuristic and novelty on hostile media perception. In Human-Computer Interaction. Theories, Methods, and Human Issues: 20th International Conference, HCI International 2018, Las Vegas, NV, USA, July 15–20, 2018, Proceedings, Part I 20 (pp. 307-324). Springer International Publishing.
    Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629-650.
    Longoni, C., & Luca, C. (2020). When do we trust AI’s recommendations more than people’s?. Harvard Business Review. https://hbr.org/2020/10/when-do-we-trust-aisrecommendations-more-than-peoples.
    Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 937-947
    Lv, X., Liu, Y., Luo, J., Liu, Y., & Li, C. (2021). Does a cute artificial intelligence assistant soften the blow? The impact of cuteness on customer tolerance of assistant service failure. Annals of Tourism Research, 87, 103114.
    Mani, R. N. (2023, December 29). Coca-Cola’s Create Real Magic AI campaign: Lessons for CIOs. CIO. https://www.cio.com/article/654859/coca-colas-create-real-magic-ai-campaign-lessons-for-cios.html
    Miller, K. M., Hofstetter, R., Krohmer, H., & Zhang, Z. J. (2011). How should consumers’ willingness to pay be measured? An empirical comparison of state-of-the-art approaches. Journal of Marketing Research, 48(1), 172-184.  
    Mouritzen, S. L. T., Penttinen, V., & Pedersen, S. (2023). Virtual influencer marketing: The good, the bad and the unreal. European Journal of Marketing, 58(2), 410-440.  
    Park, S. S., Tung, C. D., & Lee, H. (2021). The adoption of AI service robots: A comparison between credence and experience service settings. Psychology & Marketing, 38(4), 691-703.  
    Pulizzi, J. (2012). The rise of storytelling as the new marketing. Publishing Research Quarterly, 28(2), 116-123.
    Rahim, K., & Clemens, B. (2012). Organizational goals and performance measurement criteria for content marketing. Journal of Communication and Computer, 9(8), 896-904.
    Sadik-Rozsnyai, O., & Bertrandias, L. (2019). New technological attributes and willingness to pay: The role of social innovativeness. European Journal of Marketing, 53(6), 1099-1124.  
    Schmidt, J., & Bijmolt, T. H. (2020). Accurately measuring willingness to pay for consumer goods: A meta-analysis of the hypothetical bias. Journal of the Academy of Marketing Science, 48, 499-518.  
    Shirai, M., & Bettman, J. R. (2005). Consumer expectations concerning timing and depth of the next deal. Psychology & Marketing, 22(6), 457-472.  
    Snyder, C. R., & Fromkin, H. L. (1980). Theory of uniqueness. In C. R. Snyder & H. L. Fromkin (Eds.), Uniqueness: The human pursuit of difference (pp. 31-55). Plenum Press.
    Steiner, M., Eggert, A., Ulaga, W., & Backhaus, K. (2016). Do customized service packages impede value capture in industrial markets?. Journal of the Academy of Marketing Science, 44, 151-165.
    Sundar, S. S. (2020). Rise of machine agency: A framework for studying the psychology of human–AI interaction (HAII). Journal of Computer-Mediated Communication, 25(1), 74-88.
    Sundar, S. S., & Kim, J. (2019). Machine heuristic: When we trust computers more than humans with our personal information. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-9).
    Spanjol, J., & Noble, C. H. (2023). From the Editors: Engaging with generative artificial intelligence technologies in innovation management research—Some answers and more questions. Journal of Product Innovation Management, 40(4).
    Taiminen, K., & Karjaluoto, H. (2017). Examining the performance of brand-extended thematic-content: The divergent impact of avid-and skim-reader groups. Computers in Human Behavior, 72, 449-458.  
    Terho, H., Mero, J., Siutla, L., & Jaakkola, E. (2022). Digital content marketing in business markets: Activities, consequences, and contingencies along the customer journey. Industrial Marketing Management, 105, 294-310.  
    The HEINEKEN Company. (2024, March 19). HEINEKEN launches Global GenAI Lab in Singapore. https://www.theheinekencompany.com/newsroom/heineken-launches-global-genai-lab-in-singapore/
    Tian, K. T., Bearden, W. O., & Hunter, G. L. (2001). Consumers' need for uniqueness: Scale development and validation. Journal of Consumer Research, 28(1), 50-66.  
    Visentin, M., Pizzi, G., & Pichierri, M. (2019). Fake news, real problems for brands: The impact of content truthfulness and source credibility on consumers’ behavioral intentions toward the advertised brands. Journal of Interactive Marketing, 45(1), 99-112.  
    Wahid, R., Karjaluoto, H., Taiminen, K., & Asiati, D. I. (2023). Becoming TikTok famous: Strategies for global brands to engage consumers in an emerging market. Journal of International Marketing, 31(1), 106-123.
    Wahid, R., Mero, J., & Ritala, P. (2023). Written by ChatGPT, illustrated by Midjourney: Generative AI for content marketing. Asia Pacific Journal of Marketing and Logistics, 35(8), 1813-1822.  
    Wang, S. (2021). Moderating uncivil user comments by humans or machines? The effects of moderation agent on perceptions of bias and credibility in news content. Digital Journalism, 9(1), 64-83.
    Wen, Y., & Laporte, S. (2024). Experiential narratives in marketing: A comparison of generative AI and human content. Journal of Public Policy & Marketing, 07439156241297973.
    Wertenbroch, K., & Skiera, B. (2002). Measuring consumers' willingness to pay at the point of purchase. Journal of Marketing Research, 39(2), 228-241.  
    Woodruff, R. B. (1997). Customer value: The next source for competitive advantage. Journal of the Academy of Marketing Science, 25, 139-153.  
    Wu, L., & Wen, T. J. (2021). Understanding AI advertising from the consumer perspective: What factors determine consumer appreciation of AI-created advertisements?. Journal of Advertising Research, 61(2), 133-146.  
    Wu, W. Y., Lu, H. Y., Wu, Y. Y., & Fu, C. S. (2012). The effects of product scarcity and consumers’ need for uniqueness on purchase intention. International Journal of Consumer Studies, 36(3), 263-274.  
    Yeomans, M., Shah, A., Mullainathan, S., & Kleinberg, J. (2019). Making sense of recommendations. Journal of Behavioral Decision Making, 32(4), 403-414.  
    Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2-22.  
    Zhang, H., Bai, X., & Ma, Z. (2022). Consumer reactions to AI design: Exploring consumers' willingness to pay for AI‐designed products. Psychology & Marketing, 39(11), 2171-2183.
    Description: 碩士
    國立政治大學
    國際經營管理英語碩士學位學程(IMBA)
    112933027
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0112933027
    Data Type: thesis
    Appears in Collections:[國際經營管理英語碩士學程IMBA] 學位論文

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