IDEAS home Printed from https://ideas.repec.org/a/zbw/afmpwm/335560.html

Generative AI in product design: Investigating the effects of AI creatorship disclosure on consumer responses

Author

Listed:
  • Peter, Julia
  • Wannow, Stefanie
  • Haupt, Martin

Abstract

Recent technological advancements in the field of generative artificial intelligence (GAI) have led to tremendous opportunities for businesses in both content (e.g., with ChatGPT) and image creation (e.g., with Midjourney or DALL-E). Creative contentcreation such as painting or music production was once exclusively associated with human intelligence but can now be performed autonomously with these AI-tools. However, AI declaration is expected to become a legal requirement in many countries and AI-transparency might lead to adverse consumer reactions. Therefore, marketing scholars and managers are still unsure to which extent they should implement AI in their marketing and product design processes. Based on the concept of algorithm aversion (Castelo et al., 2019) and an online experiment (n = 213) with an AI-created product, this study explores the effects of AI (vs. human) as product designer on consumer responses. Moreover, we investigate the role of product design dimensions (i.e., aesthetics, functionality, symbolism, and creativity) as mediating factors. Based on the literature review and empirical analysis, a high potential of generative AI to transform marketing and product design can be diagnosed. However, at present this potential is accompanied by negative consumer responses to AI-created product designs. Implications for theory and practice are discussed.

Suggested Citation

  • Peter, Julia & Wannow, Stefanie & Haupt, Martin, 2024. "Generative AI in product design: Investigating the effects of AI creatorship disclosure on consumer responses," PraxisWissen - German Journal of Marketing, AfM – Arbeitsgemeinschaft für Marketing, vol. 9(01/2024), pages 103-123.
    • Handle: RePEc:zbw:afmpwm:335560
    DOI: 10.15459/95451.69
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/335560/1/1950059952.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.15459/95451.69?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Dayuan Li & Zhuang Pan & Ding Wang & Lu Zhang, 2023. "A study on artificial intelligence orientation and new venture performance," Asia Pacific Business Review, Taylor & Francis Journals, vol. 29(4), pages 967-989, August.
    2. Weiru Chen & Wu He & Jiayue Shen & Xin Tian & Xianping Wang, 2023. "Systematic analysis of artificial intelligence in the era of industry 4.0," Journal of Management Analytics, Taylor & Francis Journals, vol. 10(1), pages 89-108, January.
    3. Andreas Graefe & Nina Bohlken, 2020. "Automated Journalism: A Meta-Analysis of Readers’ Perceptions of Human-Written in Comparison to Automated News," Media and Communication, Cogitatio Press, vol. 8(3), pages 50-59.
    4. John Hulland & Hans Baumgartner & Keith Marion Smith, 2018. "Marketing survey research best practices: evidence and recommendations from a review of JAMS articles," Journal of the Academy of Marketing Science, Springer, vol. 46(1), pages 92-108, January.
    5. Xueming Luo & Siliang Tong & Zheng Fang & Zhe Qu, 2019. "Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases," Marketing Science, INFORMS, vol. 38(6), pages 937-947, November.
    6. Peres, Renana & Schreier, Martin & Schweidel, David & Sorescu, Alina, 2023. "On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice," International Journal of Research in Marketing, Elsevier, vol. 40(2), pages 269-275.
    7. Thomas Davenport & Abhijit Guha & Dhruv Grewal & Timna Bressgott, 2020. "How artificial intelligence will change the future of marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 24-42, January.
    8. Andreas Graefe & Nina Bohlken, 2020. "Automated Journalism: A Meta-Analysis of Readers’ Perceptions of Human-Written in Comparison to Automated News," Media and Communication, Cogitatio Press, vol. 8(3), pages 50-59.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Martin Haupt & Jan Freidank & Alexander Haas, 2025. "Consumer responses to human-AI collaboration at organizational frontlines: strategies to escape algorithm aversion in content creation," Review of Managerial Science, Springer, vol. 19(2), pages 377-413, February.
    2. Sullivan, Yulia & Fosso Wamba, Samuel, 2024. "Artificial intelligence and adaptive response to market changes: A strategy to enhance firm performance and innovation," Journal of Business Research, Elsevier, vol. 174(C).
    3. Chatterjee, Sheshadri & Chaudhuri, Ranjan & Vrontis, Demetris, 2022. "AI and digitalization in relationship management: Impact of adopting AI-embedded CRM system," Journal of Business Research, Elsevier, vol. 150(C), pages 437-450.
    4. Paola Cillo & Gaia Rubera, 2025. "Generative AI in innovation and marketing processes: A roadmap of research opportunities," Journal of the Academy of Marketing Science, Springer, vol. 53(3), pages 684-701, May.
    5. Darima Fotheringham & Michael A. Wiles, 2023. "The effect of implementing chatbot customer service on stock returns: an event study analysis," Journal of the Academy of Marketing Science, Springer, vol. 51(4), pages 802-822, July.
    6. Leah Warfield Smith & Randall Lee Rose & Alex R. Zablah & Heath McCullough & Mohammad “Mike” Saljoughian, 2023. "Examining post-purchase consumer responses to product automation," Journal of the Academy of Marketing Science, Springer, vol. 51(3), pages 530-550, May.
    7. Celiktutan, Begum & Klesse, Anne-Kathrin & Tuk, Mirjam A., 2024. "Acceptability lies in the eye of the beholder: Self-other biases in GenAI collaborations," International Journal of Research in Marketing, Elsevier, vol. 41(3), pages 496-512.
    8. Jana Holthöwer & Jenny Doorn, 2023. "Robots do not judge: service robots can alleviate embarrassment in service encounters," Journal of the Academy of Marketing Science, Springer, vol. 51(4), pages 767-784, July.
    9. Mari, Alex & Mandelli, Andreina & Algesheimer, René, 2024. "Empathic voice assistants: Enhancing consumer responses in voice commerce," Journal of Business Research, Elsevier, vol. 175(C).
    10. Zhang, Yaqiong & Wang, Shifu, 2023. "The influence of anthropomorphic appearance of artificial intelligence products on consumer behavior and brand evaluation under different product types," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    11. Mustak, Mekhail & Salminen, Joni & Plé, Loïc & Wirtz, Jochen, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 389-404.
    12. Serra-Simón, Jordi & Puntí-Brun, Mònica & Espinosa-Mirabet, Sílvia & de Faria Nogueira, Maria Alice & Martín-Guart, Ramón & Tôrres de Azevedo, Sandro, 2025. "Generative artificial intelligence in advertising. Field applications in Rio de Janeiro and Catalonia," Telecommunications Policy, Elsevier, vol. 49(8).
    13. Wenkai Zhou & Chi Zhang & Linwan Wu & Meghana Shashidhar, 2023. "ChatGPT and marketing: Analyzing public discourse in early Twitter posts," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 693-706, December.
    14. Chan, Hau-Ling & Choi, Tsan-Ming, 2025. "Using generative artificial intelligence (GenAI) in marketing: Development and practices," Journal of Business Research, Elsevier, vol. 191(C).
    15. Pantano, Eleonora & Pizzi, Gabriele, 2020. "Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    16. Rese, Alexandra & Witthohn, Lennart, 2025. "Recovering customer satisfaction after a chatbot service failure – The effect of gender," Journal of Retailing and Consumer Services, Elsevier, vol. 84(C).
    17. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
    18. Grewal, Dhruv & Guha, Abhijit & Satornino, Cinthia B. & Schweiger, Elisa B., 2021. "Artificial intelligence: The light and the darkness," Journal of Business Research, Elsevier, vol. 136(C), pages 229-236.
    19. Zhang, Fan & Pan, Jieyi, 2025. "Imitation: Mitigating AI backfire," Journal of Business Research, Elsevier, vol. 193(C).
    20. Shahriar Akter & Saida Sultana & Marcello Mariani & Samuel Fosso Wamba & Konstantina Spanaki & Yogesh Dwivedi, 2023. "Advancing algorithmic bias management capabilities in AI-driven marketing analytics research," Post-Print hal-04194438, HAL.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:afmpwm:335560. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://arbeitsgemeinschaft.marketing/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.