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Influence of human versus AI recommenders: The roles of product type and cognitive processes

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  • Wien, Anders Hauge
  • Peluso, Alessandro M.

Abstract

Previous research suggests that consumers would listen more to product recommendations from other consumers (human recommenders) than from systems based on artificial intelligence (AI recommenders). We hypothesize that this might depend on the type of product being recommended, and propose an underlying process driving this effect. Three experiments show that, for hedonic products (but not for utilitarian products), human recommenders are more effective than AI recommenders in influencing consumer reactions toward the recommended product. This effect occurs because, when compared to AI recommenders, human recommenders elicit stronger mentalizing responses in consumers. This, in turn, helps consumers self-reference the product to their own needs. However, humanizing AI recommenders increases mentalizing and self-referencing responses, thus increasing the effectiveness of this type of recommenders for hedonic products. Together, these findings provide insight into when and why consumers might rely more on product recommendations from humans as compared to AI recommenders.

Suggested Citation

  • Wien, Anders Hauge & Peluso, Alessandro M., 2021. "Influence of human versus AI recommenders: The roles of product type and cognitive processes," Journal of Business Research, Elsevier, vol. 137(C), pages 13-27.
  • Handle: RePEc:eee:jbrese:v:137:y:2021:i:c:p:13-27
    DOI: 10.1016/j.jbusres.2021.08.016
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    8. Yang, Yikai & Zheng, Jiehui & Yu, Yining & Qiu, Yiling & Wang, Lei, 2024. "The role of recommendation sources and attribute framing in online product recommendations," Journal of Business Research, Elsevier, vol. 174(C).
    9. Zhu, Yimin & Zhang, Jiemin & Wu, Jifei & Liu, Yingyue, 2022. "AI is better when I'm sure: The influence of certainty of needs on consumers' acceptance of AI chatbots," Journal of Business Research, Elsevier, vol. 150(C), pages 642-652.
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    11. Ransome Epie Bawack & Emilie Bonhoure, 2023. "Influencer is the New Recommender: insights for Theorising Social Recommender Systems," Information Systems Frontiers, Springer, vol. 25(1), pages 183-197, February.
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    13. Meng, Lu (Monroe) & Bie, Yongyue & Yang, Mengya & Wang, Yijie, 2024. "Watching it motivates me to become stronger: Virtual influencers' impact on consumer self-improvement product preferences," Journal of Business Research, Elsevier, vol. 178(C).
    14. Armenia, Stefano & Franco, Eduardo & Iandolo, Francesca & Maielli, Giuliano & Vito, Pietro, 2024. "Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    15. Hai Lan & Xiaofei Tang & Yong Ye & Huiqin Zhang, 2024. "Abstract or concrete? The effects of language style and service context on continuous usage intention for AI voice assistants," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    16. Jansen, Thomas & Moura, Francisco Tigre, 2024. "WOM, eWOM and WOMachine: The evolution of consumer recommendations through a systematic review of 194 studies," IU Discussion Papers - Marketing & Communication 3 (Juni 2024), IU International University of Applied Sciences.

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