The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
The Caring Machine: Feeling AI for Customer Care
Open accessAbstract
Customer care is important for its role in relationship building. This role has traditionally been performed by human customer agents; however, the emergence of interactive generative AI (GenAI) shows potential for using AI for customer care in emotionally charged interactions. Bridging practice and the academic literatures in marketing and computer science, this article develops an AI-enabled customer care journey, from accurate emotion recognition to empathetic response, emotional management support, and, finally, the establishment of an emotional connection. Marketing requirements for each of the stages are derived from in-depth interviews with top managers and a survey of chief marketing officers. By juxtaposing these requirements against the current feeling capabilities of GenAI, the authors highlight the technological challenges engineers must tackle. The article concludes with a set of marketing tenets for implementing and researching the caring machine. These include verifying emotion recognition accuracy using marketing emotion theories through multiple emotion signals and methods, utilizing prompt engineering to enhance GenAI’s emotion understanding, employing “response engineering” to personalize emotion management recommendations, and strategically deploying GenAI for emotional connection to simultaneously enhance customer emotional well-being and customer lifetime value.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1994) · Journal of the American Dietetic Association · doi:10.1016/0002-8223(94)92216-0 · Cited by 45,910
Lazarus, R. S. (1991) · doi:10.1093/oso/9780195069945.001.0001 · Cited by 12,691
Measuring individual differences in empathy: Evidence for a multidimensional approach.
Davis, M. H. (1983) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.44.1.113 · Cited by 10,272
Picard, R. W. (1997) · MIT Press · doi:10.7551/mitpress/1140.001.0001 · Cited by 7,382
Understanding customer experience throughout the customer journey
Lemon, K. N., & Verhoef, P. C. (2016) · Journal of Marketing · doi:10.1509/jm.15.0420 · Cited by 5,765
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025 · cited 1×
“Ji et al. (2023) de fi ne hallu-cination in GenAI as generated output that is nonsensical or unfaithful to the input data (i.e., output does not exist in the source input), but it gives users the perception of being fl uent and natural.”
Artificial intelligence in service
Huang, M.-H., & Rust, R. T. (2018) · Journal of Service Research · doi:10.1177/1094670517752459 · Cited by 3,978
Self-citationThe Role of Emotions in Marketing
Bagozzi, R. P., Gopinath, M., & Nyer, P. U. (1999) · Journal of the Academy of Marketing Science · doi:10.1177/0092070399272005 · Cited by 3,349
Emotional Labor in Service Roles: The Influence of Identity
Ashforth, B. E., & Humphrey, R. H. (1993) · The Academy of Management Review · doi:10.2307/258824 · Cited by 3,267
The Ties That Bind: Measuring the Strength of Consumers’ Emotional Attachments to Brands
Thomson, M., MacInnis, D. J., & Whan Park, C. (2005) · Journal of Consumer Psychology · doi:10.1207/s15327663jcp1501_10 · Cited by 2,713 · cited 2×
“Thomson, MacInnis, and Park (2005, p. 78) develop a scale to measure consumers ’ emotional attachments to brands.”
Cited by
Gelbrich, K., Roschk, H., Miederer, S., & Kerath, A. (2026) · Journal of Marketing · doi:10.1177/00222429251344139 · Cited by 38
AI for Customer Journeys: A Transformer Approach
Lu, Z., & Kannan, P. K. (2026) · Journal of Marketing Research · doi:10.1177/00222437251347268 · Cited by 21
Reducing prejudice with counter‐stereotypical AI
Hermann, E., De Freitas, J., & Puntoni, S. (2025) · Consumer Psychology Review · doi:10.1002/arcp.1102 · Cited by 21
Stylos, N., Okumus, F., & Onder, I. (2025) · Tourism Management · doi:10.1016/j.tourman.2025.105205 · Cited by 18
Generative AI in Marketing: Foundations, Trends, and Future Research Propositions
Prasanna, A., & Kushwaha, B. P. (2025) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/5542513 · Cited by 10
Metadata
- Authors
- Huang, M.-H., & Rust, R. T.
- Venue
- Journal of Marketing
- Publisher
- SAGE Publications
- Published
- 2024-03-22
- Volume
- 88
- Issue
- 5
- Pages
- 1-23
- ISSN
- 0022-2429 (ISSN-L)
- 1547-7185
- OpenAlex
- W4390051025
- Cited by
- 312
- References
- 76
- FWCI
- 27.41
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The Caring Machine: Feeling AI for Customer Care. https://app.cochat.ai/papers/10.1177/00222429231224748
- The paper
- Huang, M.-H., & Rust, R. T. (2024). The Caring Machine: Feeling AI for Customer Care. Journal of Marketing. https://doi.org/10.1177/00222429231224748
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.