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Cited by 22

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

How warm-versus competent-toned AI apologies affect trust and forgiveness through emotions and perceived sincerity

Lim, Joon Soo, Hong, Nalae, Schneider, Erika·2025·Computers in Human Behavior
DOI 10.1016/j.chb.2025.108761
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Research works printed but not linked: 44

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Metadata

Authors
Lim, J. S., Hong, N., & Schneider, E.
Venue
Computers in Human Behavior
Publisher
Elsevier BV
Published
2025-11
Volume
172
Pages
108761
ISSN
0747-5632
DOI
10.1016/j.chb.2025.108761
Cited by
22
References
4
Language
en
Licence
Funders
Syracuse University

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CoChat. (2026). Citation check: How warm-versus competent-toned AI apologies affect trust and forgiveness through emotions and perceived sincerity. https://app.cochat.ai/papers/10.1016/j.chb.2025.108761
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Lim, J. S., Hong, N., & Schneider, E. (2025). How warm-versus competent-toned AI apologies affect trust and forgiveness through emotions and perceived sincerity. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2025.108761

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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.