The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Frontline encounters of the AI kind: An evolved service encounter framework
Open accessAbstract
Abstract Artificial intelligence (AI) is radically transforming frontline service encounters, with AI increasingly playing the role of employee or customer. Programmed to speak or write like a human, AI is poised to usher in a frontline service revolution. No longer will frontline encounters between customer and employee be simply human-to-human; rather, researchers must consider an evolved paradigm where each actor could be either human or AI. Further complicating this 2 × 2 framework is whether the human, either customer or employee, recognizes when they are interacting with a non-human exchange partner. Accordingly, we develop an evolved service encounter framework and, in doing so, introduce the concept of counterfeit service, interspecific service (AI-to-human), interAI service (AI-to-AI), and offer a research agenda focused on the implementation of AI in dyadic service exchanges.
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
A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions
Oliver, R. L. (1980) · Journal of Marketing Research · doi:10.1177/002224378001700405 · Cited by 14,494
The future of employment: How susceptible are jobs to computerisation?
Frey, C. B., & Osborne, M. A. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.08.019 · Cited by 8,727
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
Relationship Quality in Services Selling: An Interpersonal Influence Perspective
Crosby, L. A., Evans, K. R., & Cowles, D. (1990) · Journal of Marketing · doi:10.1177/002224299005400306 · Cited by 4,763
Machines and Mindlessness: Social Responses to Computers
Nass, C., & Moon, Y. (2000) · Journal of Social Issues · doi:10.1111/0022-4537.00153 · Cited by 4,022 · cited 1×
“Research has repeatedly observed individuals “mindlessly” apply social rules to computers and other artificial entities, especially when these entities display human characteristics or engage the user in social interactions (Hertz & Wiese, 2018; Moon, 2003; Nass & Moon, 2000).”
BackgroundArtificial intelligence in service
Huang, M.-H., & Rust, R. T. (2018) · Journal of Service Research · doi:10.1177/1094670517752459 · Cited by 3,978
Emotional regulation in the workplace: A new way to conceptualize emotional labor.
Grandey, A. A. (2000) · Journal of Occupational Health Psychology · doi:10.1037//1076-8998.5.1.95 · Cited by 3,817
Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making
Jarrahi, M. H. (2018) · Business Horizons · doi:10.1016/j.bushor.2018.03.007 · Cited by 2,772
Jones, E. E., & Harris, V. A. (1967) · Journal of Experimental Social Psychology · doi:10.1016/0022-1031(67)90034-0 · Cited by 1,783 · cited 1×
“However, one must also consider “fundamental attribution error” (Jones & Harris, 1967) whereby individuals attribute their own bad behavior to external forces (e.g., I am too busy) and other people's bad behavior to internal factors (e.g., they are a jerk).”
BackgroundService research priorities in a rapidly changing context
Ostrom, A. L., Parasuraman, A., Bowen, D. E., Patrício, L., & Voss, C. A. (2015) · Journal of Service Research · doi:10.1177/1094670515576315 · Cited by 1,469
Cited by
AI-powered marketing: What, where, and how?
Kumar, V., Ashraf, A. R., & Nadeem, W. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102783 · Cited by 509
Chi, O. H., Denton, G., & Gursoy, D. (2020) · Journal of Hospitality Marketing & Management · doi:10.1080/19368623.2020.1721394 · Cited by 334
(2021) · doi:10.2196/25856 · Cited by 268
AI-chatbots on the services frontline addressing the challenges and opportunities of agency
Chong, T., Yu, T., Keeling, D. I., & de Ruyter, K. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2021.102735 · Cited by 245
How may I help you? Driving brand engagement through the warmth of an initial chatbot message
Kull, A. J., Romero, M., & Monahan, L. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.03.005 · Cited by 237
Metadata
- Authors
- Robinson, S., Orsingher, C., Alkire, L., De Keyser, A., Giebelhausen, M., Papamichail, K. N., Shams, P., & Temerak, M. S.
- Venue
- Journal of Business Research
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 116
- Pages
- 366-376
- ISSN
- 0148-2963 (ISSN-L)
- OpenAlex
- W2981611507
- Cited by
- 207
- References
- 15
- FWCI
- 9.44
- Type
- journal article
- Language
- en
- Open access
- green
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: Frontline encounters of the AI kind: An evolved service encounter framework. https://app.cochat.ai/papers/10.1016/j.jbusres.2019.08.038
- The paper
- Robinson, S., Orsingher, C., Alkire, L., De Keyser, A., Giebelhausen, M., Papamichail, K. N., Shams, P., & Temerak, M. S. (2020). Frontline encounters of the AI kind: An evolved service encounter framework. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2019.08.038
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.
