The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Evaluations of an artificial intelligence instructor's voice: Social Identity Theory in human-robot interactions
Abstract
Abstract This study employs the Computers are Social Actors (CASA) paradigm to extend the predictions of Social Identity Theory (SIT) to human-robot interaction (HRI) in the context of instructional communication. SIT posits that individuals gain a sense of personal worth from the groups with which they identify. Previous research has demonstrated that age group identification is meaningful to individuals’ self-concepts. Results demonstrated that higher age identified students rated the older A.I. voice instructor (representing an out-group member) higher for credibility and social presence and reported more motivation to learn than those students with low age identification. Implications are discussed for SIT and design features of computerized voices.
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Cites
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Are Machines Gender Neutral? Gender‐Stereotypic Responses to Computers With Voices
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Anthropomorphism, agency, and ethopoeia: computers as social actors
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Arsenyan, J., & Mirowska, A. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102694 · Cited by 566
ChatGPT and consumers: Benefits, Pitfalls and Future Research Agenda
Paul, J., Ueno, A., & Dennis, C. (2023) · International Journal of Consumer Studies · doi:10.1111/ijcs.12928 · Cited by 345
Kim, J., Merrill, K., Xu, K., & Sellnow, D. D. (2020) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2020.1801227 · Cited by 288
Human-machine collaboration in managerial decision making
Haesevoets, T., De Cremer, D., Dierckx, K., & Van Hiel, A. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106730 · Cited by 280
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Metadata
- Authors
- Edwards, C., Edwards, A., Stoll, B., Lin, X., & Massey, N.
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 90
- Pages
- 357-362
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W2887919024
- Cited by
- 279
- References
- 8
- FWCI
- 20.62
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
