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User trust in artificial intelligence: A comprehensive conceptual framework
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Many factors outside the XAI system itself can influence user trust.
“As said, there are many factors outside of the XAI system itself that can influence user trust.”
From Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation (2025) · cites this paper as R. Yang & Wibowo, 2022
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Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019) · International Journal of Public Administration · doi:10.1080/01900692.2018.1498103 · Cited by 1,341 · cited 4×
“The role of organizational factors may vary because organizations apply AI technologies in different contexts (e.g. industry context) (Araujo et al., 2020; Kolasinska et al., 2019; Lobschat et al., 2021; Wirtz et al., 2019).”
BackgroundInfluentialData governance: Organizing data for trustworthy Artificial Intelligence
Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020) · Government Information Quarterly · doi:10.1016/j.giq.2020.101493 · Cited by 811 · cited 5×
“Thus, societal expectations, norms, and values affect the policies, principles, and procedures (e.g. data governance) of organizations that adopt AI technologies (Janssen et al., 2020).”
BackgroundInfluentialMedical students' attitude towards artificial intelligence: a multicentre survey
Pinto dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., Maintz, D., & Baeßler, B. (2019) · European Radiology · doi:10.1007/s00330-018-5601-1 · Cited by 754
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Attachment and trust in artificial intelligence
Gillath, O., Ai, T., Branicky, M. S., Keshmiri, S., Davison, R. B., & Spaulding, R. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106607 · Cited by 448 · cited 17×
“…risk as an antecedent of user trust in AI, some studies have reported a reversed relationship between these two constructs, specifically that the attenuated perceived risk is one of the outcomes of building user trust (Bedué & Fritzsche, 2021; Gillath et al., 2021; Mou et al., 2017; Sutrop, 2019).”
BackgroundResultInfluentialSystematic Review: Trust-Building Factors and Implications for Conversational Agent Design
Rheu, M., Shin, J. Y., Peng, W., & Huh-Yoo, J. (2021) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2020.1807710 · Cited by 360
Yen, C., & Chiang, M.-C. (2021) · Behaviour & Information Technology · doi:10.1080/0144929x.2020.1743362 · Cited by 297
National culture and consumer trust in e-commerce
Hallikainen, H., & Laukkanen, T. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2017.07.002 · Cited by 287 · cited 7×
“Thus, people from collectivist groups are more likely to trust others, especially their ability (Hallikainen & Laukkanen, 2018; Robinson, 2020).”
BackgroundInfluentialRecommendations for the ethical use and design of artificial intelligent care providers
Luxton, D. D. (2014) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2014.06.004 · Cited by 243
Why different trust relationships matter for information systems users
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Perspectives on consumer decision making: an integrated approach
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“In other words, people will use simpler decision rules when they believe some AI applications are less valuable or important (Bauer et al., 2006; Hansen, 2005).”
BackgroundExplanation and trust: what to tell the user in security and AI?
Pieters, W. (2011) · Ethics and Information Technology · doi:10.1007/s10676-010-9253-3 · Cited by 153
Trustworthy Augmented Intelligence in Health Care
Crigger, E., Reinbold, K., Hanson, C., Kao, A., Blake, K., & Irons, M. (2022) · Journal of Medical Systems · doi:10.1007/s10916-021-01790-z · Cited by 132
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Metadata
- Authors
- Yang, R., & Wibowo, S.
- ORCID
- Yang, Rongbin
- Venue
- Electronic Markets
- Published
- 2022-11-04
- Volume
- 32
- Issue
- 4
- Pages
- 2053-2077
- ISSN
- 1019-6781
- 1422-8890
- Cited by
- 170
- References
- 15
- Language
- en
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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.
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