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Chatbot ads with a human touch: A test of anthropomorphism, interactivity, and narrativity

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  • Sun, Yuan
  • Chen, Jin
  • Sundar, S. Shyam

Abstract

Powered by artificial intelligence (AI), chatbots are increasingly capable of simulating human-like conversations. But, is this desirable for strategic communications? Will chatbots be more persuasive if they are more human-like, not only in their appearance but also in their interaction and delivery of advertising content? We explored these questions with a 2 (chatbot profile: human-like vs. machine-like) x 2 (message interactivity: high vs. low) x 2 (ad type: narrative vs. factual) experiment (N = 414). Data reveal that high message interactivity fosters positive attitudes toward the chatbot and the ad by mitigating violated expectancy. Narrative ads promote chatbot advertising through perceived transportation. A three-way interaction revealed that when a chatbot is machine-like in appearance, higher interactivity and adoption of a narrative style of delivery serve to increase ad persuasiveness by heightening social presence. Theoretical and practical implications for chatbot advertising are discussed.

Suggested Citation

  • Sun, Yuan & Chen, Jin & Sundar, S. Shyam, 2024. "Chatbot ads with a human touch: A test of anthropomorphism, interactivity, and narrativity," Journal of Business Research, Elsevier, vol. 172(C).
    • Handle: RePEc:eee:jbrese:v:172:y:2024:i:c:s0148296323007622
    DOI: 10.1016/j.jbusres.2023.114403
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    1. Polyxeni Vassilakopoulou & Arve Haug & Leif Martin Salvesen & Ilias O. Pappas, 2023. "Developing human/AI interactions for chat-based customer services: lessons learned from the Norwegian government," European Journal of Information Systems, Taylor & Francis Journals, vol. 32(1), pages 10-22, January.
    2. van Laer, Tom & Feiereisen, Stephanie & Visconti, Luca M., 2019. "Storytelling in the digital era: A meta-analysis of relevant moderators of the narrative transportation effect," Journal of Business Research, Elsevier, vol. 96(C), pages 135-146.
    3. Perracchio, Laura A & Meyers-Levy, Joan, 1997. "Evaluating Persuasion-Enhancing Techniques from a Resource-Matching Perspective," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 24(2), pages 178-191, September.
    4. Grigsby, Jamie L. & Mellema, Hillary N., 2020. "Negative Consequences of Storytelling in Native Advertising," Journal of Interactive Marketing, Elsevier, vol. 52(C), pages 61-78.
    5. Samuelson, William & Zeckhauser, Richard, 1988. "Status Quo Bias in Decision Making," Journal of Risk and Uncertainty, Springer, vol. 1(1), pages 7-59, March.
    6. Markus Blut & Cheng Wang & Nancy V. Wünderlich & Christian Brock, 2021. "Understanding anthropomorphism in service provision: a meta-analysis of physical robots, chatbots, and other AI," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 632-658, July.
    7. Deighton, John & Romer, Daniel & McQueen, Josh, 1989. "Using Drama to Persuade," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(3), pages 335-343, December.
    8. Roy, Rajat & Naidoo, Vik, 2021. "Enhancing chatbot effectiveness: The role of anthropomorphic conversational styles and time orientation," Journal of Business Research, Elsevier, vol. 126(C), pages 23-34.
    9. Heckman, James J, 1990. "Varieties of Selection Bias," American Economic Review, American Economic Association, vol. 80(2), pages 313-318, May.
    10. 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.
    11. Kathryn Sharpe Wessling & Joel Huber & Oded Netzer, 2017. "MTurk Character Misrepresentation: Assessment and Solutions," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(1), pages 211-230.
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