The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
AI at everyone’s fingertips? Identifying the predictors of health information seeking intentions using AI
Open accessAbstract
The advent of AI tools has the potential to transform how health information is sought, which increases the need for insights into why individuals turn to AI. The study, guided by channel-specific predictors covered by the Planned Risk Information Seeking Model, additionally considers trust and risk perceptions toward AI, and examines the predictors of one’s AI-seeking intention. The results of an online survey (N = 1,121) indicated that attitudes toward seeking, seeking-related norms, and perceived seeking control were significant predictors of individuals’ intention to seek health information from AI. In contrast, trust and risk perceptions were of minor relevance.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“there is an emerging field developing around artificial intelligence (AI)-HISB [16-18].”
From Evolving Health Information–Seeking Behavior in the Context of Google AI Overviews, ChatGPT, and Alexa: Interview Study Using the Think-Aloud Protocol (2025) · cites this paper as [16]
In the citing paper
“This is in line with other recent research on AI-HISB, which is starting to demonstrate that a person’s likelihood to use AI is not necessarily based on how much they trust the technology [16].”
From Evolving Health Information–Seeking Behavior in the Context of Google AI Overviews, ChatGPT, and Alexa: Interview Study Using the Think-Aloud Protocol (2025) · cites this paper as [16]
Citations
Cites
PRISM: A Planned Risk Information Seeking Model
Kahlor, L. (2010) · Health Communication · doi:10.1080/10410231003775172 · Cited by 531
That’s a Lot to Process! Pitfalls of Popular Path Models
Rohrer, J. M., Hünermund, P., Arslan, R. C., & Elson, M. (2022) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459221095827 · Cited by 267
Research works printed but not linked: 24
- 10.1080/10810730.2018.1493058 · DOI
- 10.2196/36322 · DOI
- 10.3390/bs13090786 · DOI
- 10.1080/10510974.2020.1807380 · DOI
- 10.2196/47184 · DOI · Paper page
- 10.1080/10410236.2013.831685 · DOI
- 10.1515/9781503620766 · DOI · Paper page
- 10.1080/10705519909540118 · DOI · Paper page
- 10.1080/23311886.2017.1302785 · DOI
- Johnson, J. D., & Case, D. O. (2012). Health information seeking. health communication: Vol. 4. Lang.
- 10.1080/17524032.2019.1699136 · DOI
- 10.1371/journal.pone.0288109 · DOI
- 10.5771/2192-4007-2016-4-367 · DOI
- 10.1177/10755470241232993 · DOI
- 10.1515/commun-2021-0117 · DOI
- 10.1177/10755470241253815 · DOI
- 10.2196/25963 · DOI
- 10.3389/fpubh.2024.1310437 · DOI
- 10.1177/20539517231213823 · DOI
- 10.1080/13669877.2012.729522 · DOI
- Siau, K., & Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal, 31(2), 47–53.
- 10.1080/10410236.2012.708633 · DOI
- 10.1080/10410236.2020.1748829 · DOI
- 10.1145/3581641.3584031 · DOI · Paper page
Cited by
Sorted by citations
Wardle, C., Urbani, S., & Wang, E. (2025) · Journal of Medical Internet Research · doi:10.2196/79961 · Cited by 43
“This is in line with other recent research on AI-HISB, which is starting to demonstrate that a person’s likelihood to use AI is not necessarily based on how much they trust the technology [16].”
Metadata
- Authors
- Link, E., & Beckmann, S.
- Corresponding author
- Link, Elena
- ORCID
- Link, Elena
- Venue
- Communication Research Reports
- Publisher
- Informa UK Limited
- Published
- 2024-11-13
- Volume
- 42
- Issue
- 1
- Pages
- 1-11
- ISSN
- 0882-4096 (ISSN-L)
- 1746-4099
- OpenAlex
- W4404362718
- Cited by
- 17
- References
- 2
- Citations by year
- 2026: 11 · 2025: 6
- FWCI
- 10.03
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Communication Research Reports (publishedVersion, cc-by)
- Gutenberg Open Science (submittedVersion, cc-by-nd)
- Gutenberg Open Science (cc-by)
- Topics
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- artificial intelligence, health information seeking, trust in AI, risk perception, health information sources
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: AI at everyone’s fingertips? Identifying the predictors of health information seeking intentions using AI. https://app.cochat.ai/papers/10.1080/08824096.2024.2427609
- The paper
- Link, E., & Beckmann, S. (2025). AI at everyone’s fingertips? Identifying the predictors of health information seeking intentions using AI. Communication Research Reports. https://doi.org/10.1080/08824096.2024.2427609
