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.
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“Whereas computers exist as hardware and software, and their services are not personalized (Li & Huang, 2020).”
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“Modern computational solutions have stoked workers’ fears about technology being unethical, uncontrollable, unintelligible, unfair, and even harmful to human survival (Li & Huang, 2020).”
Background
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
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