The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory
Abstract
Abstract With the rapid development of artificial intelligence (AI), AI anxiety has emerged and is receiving widespread attention, but research on this topic is not comprehensive. Therefore, we investigated the dimensions of AI anxiety using the theoretical model of integrated fear acquisition and a questionnaire survey. A total of 494 valid questionnaires were recovered. Through a first-order confirmatory factor analysis (CFA), a factor model of AI anxiety was constructed, and eight factors of AI anxiety were verified. Then, a second-order CFA was applied to verify the adaptation of the factor structure of AI anxiety to fear acquisition. We identified four dimensions of AI anxiety and proposed a theory of AI anxiety acquisition that illustrates four pathways of AI anxiety acquisition. Each pathway includes two factors that cause AI anxiety. We conclude by analyzing the limitations of current AI anxiety research and proposing a broader research agenda for AI anxiety.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788
On the Evaluation of Structural Equation Models
Bagozzi, R. P., & Yi, Y. (1988) · Journal of the Academy of Marketing Science · doi:10.1007/bf02723327 · Cited by 26,163
Grandmaster level in StarCraft II using multi-agent reinforcement learning
Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al. (2019) · Nature · doi:10.1038/s41586-019-1724-z · Cited by 3,455
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., et al. (2019) · Nature · doi:10.1038/s41586-019-1138-y · Cited by 1,364 · cited 5×
“Furthermore, AI may even shape human behavior in the future [17].”
InfluentialThe conditioning theory of fearacquisition: A critical examination
Rachman, S. (1977) · Behaviour Research and Therapy · doi:10.1016/0005-7967(77)90041-9 · Cited by 1,191 · cited 4×
“In the source of fear conditioning theory, "anxiety" was initially used instead of "fear" [42], and scholars did not distinguish between them [39,40].”
InfluentialUnraveling the mysteries of anxiety and its disorders from the perspective of emotion theory.
Barlow, D. H. (2000) · American Psychologist · doi:10.1037/0003-066x.55.11.1247 · Cited by 1,135
Mineka, S., & Zinbarg, R. (2006) · American Psychologist · doi:10.1037/0003-066x.61.1.10 · Cited by 929
The ethics of artificial intelligence
Bostrom, N., & Yudkowsky, E. (2014) · The Cambridge Handbook of Artificial Intelligence · doi:10.1017/cbo9781139046855.020 · Cited by 918
Discrimination in online ad delivery
Sweeney, L. (2013) · Communications of the ACM · doi:10.1145/2447976.2447990 · Cited by 783 · cited 4×
“Among the distinct anxieties caused by AI, bias behavior anxiety concerns AI ’ s ability to accurately recognize differences between individuals [31,58].”
InfluentialA stimulus-response analysis of anxiety and its role as a reinforcing agent.
Mowrer, O. H. (1939) · Psychological Review · doi:10.1037/h0054288 · Cited by 728 · cited 2×
“In the source of fear conditioning theory, "anxiety" was initially used instead of "fear" [42], and scholars did not distinguish between them [39,40].”
Cited by
Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2151730 · Cited by 688
Teachers’ readiness and intention to teach artificial intelligence in schools
Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100099 · Cited by 412
Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis
Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00420-7 · Cited by 413
Higher education students’ perceptions of ChatGPT: A global study of early reactions
Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., A. Iahad, N., Abdulla, A. A., Akopyan, A., Aldana Segura, M. W., AlHumaid, J., et al. (2025) · PLOS ONE · doi:10.1371/journal.pone.0315011 · Cited by 376
Self-citationSchiavo, G., Businaro, S., & Zancanaro, M. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102537 · Cited by 284
Metadata
- Authors
- Li, J., & Huang, J.-S.
- Venue
- Technology in Society
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 63
- Pages
- 101410
- ISSN
- 0160-791X (ISSN-L)
- OpenAlex
- W3090589984
- Cited by
- 453
- References
- 17
- FWCI
- 20.26
- Type
- journal article
- Language
- en
- Open access
- closed
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: Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. https://app.cochat.ai/papers/10.1016/j.techsoc.2020.101410
- The paper
- Li, J., & Huang, J.-S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society. https://doi.org/10.1016/j.techsoc.2020.101410
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
