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Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience

Author

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  • Yuping Liu-Thompkins

    (Old Dominion University)

  • Shintaro Okazaki

    (King’s Business School, King’s College London, Bush House)

  • Hairong Li

    (Michigan State University)

Abstract

Artificial intelligence (AI) continues to transform firm-customer interactions. However, current AI marketing agents are often perceived as cold and uncaring and can be poor substitutes for human-based interactions. Addressing this issue, this article argues that artificial empathy needs to become an important design consideration in the next generation of AI marketing applications. Drawing from research in diverse disciplines, we develop a systematic framework for integrating artificial empathy into AI-enabled marketing interactions. We elaborate on the key components of artificial empathy and how each component can be implemented in AI marketing agents. We further explicate and test how artificial empathy generates value for both customers and firms by bridging the AI-human gap in affective and social customer experience. Recognizing that artificial empathy may not always be desirable or relevant, we identify the requirements for artificial empathy to create value and deduce situations where it is unnecessary and, in some cases, harmful.

Suggested Citation

  • Yuping Liu-Thompkins & Shintaro Okazaki & Hairong Li, 2022. "Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1198-1218, November.
    • Handle: RePEc:spr:joamsc:v:50:y:2022:i:6:d:10.1007_s11747-022-00892-5
    DOI: 10.1007/s11747-022-00892-5
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    Cited by:

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    2. Koh, Le Yi & Yuen, Kum Fai, 2023. "Public acceptance of autonomous vehicles: Examining the joint influence of perceived vehicle performance and intelligent in-vehicle interaction quality," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    3. Li, Sixian & Peluso, Alessandro M. & Duan, Jinyun, 2023. "Why do we prefer humans to artificial intelligence in telemarketing? A mind perception explanation," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    4. Poushneh, Atieh & Vasquez-Parraga, Arturo & Gearhart, Richard S., 2024. "The effect of empathetic response and consumers’ narcissism in voice-based artificial intelligence," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    5. Liao, Jiancai & Huang, Jingya, 2024. "Think like a robot: How interactions with humanoid service robots affect consumers’ decision strategies," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    6. Roy, Sanjit K. & Singh, Gaganpreet & Sadeque, Saalem & Gruner, Richard L., 2024. "Customer experience quality with social robots: Does trust matter?," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    7. Mari, Alex & Mandelli, Andreina & Algesheimer, René, 2024. "Empathic voice assistants: Enhancing consumer responses in voice commerce," Journal of Business Research, Elsevier, vol. 175(C).
    8. Liao, Xia & Zheng, Yu-Hao & Shi, Guicheng & Bu, Huimei, 2024. "Automated social presence in artificial-intelligence services: Conceptualization, scale development, and validation," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    9. Scholdra, Thomas P. & Wichmann, Julian R.K. & Reinartz, Werner J., 2023. "Reimagining personalization in the physical store," Journal of Retailing, Elsevier, vol. 99(4), pages 563-579.

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