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Cited by 441AI in Service InteractionsCustomer Service Quality and LoyaltyDigital Marketing and Social Media

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

Open access
Liu-Thompkins, Yuping, Okazaki, Shintaro, Li, Hairong·2022·Journal of the Academy of Marketing Science
DOI 10.1007/s11747-022-00892-5
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Abstract

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.

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Crossref · 10.1007/s11747-022-00892-5

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125 of 125 references verified0 citation errors · 0 not checkable

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  • Artificial empathy narrows the customer experience gap between AI and human agents, with high empathy levels resulting in comparable affective and social experiences to humans.

    “Previous marketing research also underscores the value of empathetic AI interactions, showing that artificial empathy narrows the customer experience gap between AI and human agents, with high empathy levels resulting in comparable affective and social experiences to humans, particularly improving social interactions (Liu-Thompkins, Okazaki, and Li 2022).”

    From AI Companions Reduce Loneliness (2024) · cites this paper as Liu-Thompkins, Okazaki, and Li 2022

  • Transformer models employ advanced attention mechanisms to achieve enhanced contextual processing, allowing for more subtle understanding and interaction.

    “They employ advanced attention mechanisms to achieve enhanced contextual processing, allowing for more subtle understanding and interaction [142], [143], [144].”

    From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [144]

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Research works printed but not linked: 11

  1. Brei, V. A. (2020). Machine learning in marketing. Foundations and Trends in Marketing, 14(3), 173-236.
  2. Dial, M. (2018). Heartificial empathy: Putting heart into business and artificial intelligence. DigitalProof Press.
  3. Ekman, P., Friesen, W. V., & Hager, J. C. (2002). The facial action cod- ing system: A technique for the measurement of facial movement. Consulting Psychologists Press.
  4. Hess, U., & Fischer, A. (2013). Emotional mimicry as social regulation. Personality and Social Psychology Bulletin, 17(2), 142-157.
  5. Kummer, N., Kadish, D., Dulic, A., & Najjaran, H. (2012). The empa- thy machine. 2012 IEEE International Conference on Systems, Man, and Cybernetics.
  6. Liu, J., & Toubia, O. (2018). A semantic approach for estimating con- sumer content preferences from online search queries. Marketing Science, 37(6), 855-882.
  7. Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machine vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 937-947.
  8. Mori, M. (2012). The Uncanny Valley. IEEE Robotics & Automation Magazine, 19(2), 98-100.
  9. Ochs, M., Niewiadomski, R., & Pelachaud, C. (2015). Facial expression of emotions for virtual characters. In R. A. Calvo, S. D'Mello, J. M. Gratch, & A. Kappas (Eds.), The Oxford handbook of affec- tive computing (pp. 261-272). Oxford University Press.
  10. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An intro- duction. MIT press.
  11. Zhou, M. X., Mark, G., Li, J., & Yang, H. (2019). Trusting virtual agents: The effect of personality. ACM Transactions on Interac- tive Intelligent Systems (TiiS), 9(2-3), 1-36.

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Related

Authors
Liu-Thompkins, Y., Okazaki, S., & Li, H.
Affiliations
  • King's College London (GB)
  • Michigan State University (US)
Corresponding author
Okazaki, Shintaro
ORCID
Venue
Journal of the Academy of Marketing Science
Publisher
Springer Science and Business Media LLC
Published
2022-07-08
Volume
50
Issue
6
Pages
1198-1218
ISSN
  • 0092-0703 (ISSN-L)
  • 1552-7824
DOI
10.1007/s11747-022-00892-5
OpenAlex
W4284966012
Cited by
441
References
125
Citations by year
2026: 143 · 2025: 147 · 2024: 90 · 2023: 51 · 2022: 10
FWCI
24.51
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of the Academy of Marketing Science (publishedVersion, cc-by)
  • Research Portal (King's College London) (acceptedVersion, cc-by)
  • ODU Digital Commons (Old Dominion University) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
  • Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
Keywords
artificial empathy, human-AI interaction, customer experience design, customer value, chatbots

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