The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
The future of service: The power of emotion in human-robot interaction
Open accessAbstract
Astoundingly, recent technological advancements have enabled robots to display emotions. Yet, while emotional expression is valued in the field of service, understanding emotions in human-robot interaction remains underexplored. Since emotions are contagious/transmittable, this study utilised Instagram data to uncover how emotional robots influence potential consumers’ affective feelings. By employing machine learning algorithms and sentiment analysis, the findings suggest that the expressions of surprise and happiness are key to creating positive impacts on potential consumers. The cross-disciplinary nature of this study lays the groundwork for next-level social, design, and creative experiences in artificial intelligence research regarding consumer service and experience contexts.
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Cited by
Meng, L. (., Duan, S., Zhao, Y., Lü, K., & Chen, S. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2021.102733 · Cited by 369
Customers’ acceptance of artificially intelligent service robots: The influence of trust and culture
Chi, O. H., Chi, C. G., Gursoy, D., & Nunkoo, R. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102623 · Cited by 268
Can AI really help? The double-edged sword effect of AI assistant on employees’ innovation behavior
Yin, M., Jiang, S., & Niu, X. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107987 · Cited by 267
Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M., & Siemens, G. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100138 · Cited by 152
Zhang, Y., & Wang, S. (2023) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2023.103432 · Cited by 102
Metadata
- Authors
- Chuah, S. H.-W., & Yu, J.
- Venue
- Journal of Retailing and Consumer Services
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 61
- Pages
- 102551
- ISSN
- 0969-6989 (ISSN-L)
- OpenAlex
- W3149101596
- Cited by
- 265
- References
- 89
- FWCI
- 16.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
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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.
