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The roles of personality traits, AI anxiety, and demographic factors in attitudes towards artificial intelligence
Kaya, Feridun Aydin, Fatih Schepman, Astrid Rodway, Paul Yetişensoy, Okan Demir Kaya, Meva
Kaya, Feridun
Aydin, Fatih
Schepman, Astrid
Rodway, Paul
Yetişensoy, Okan
Demir Kaya, Meva
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Publication Date
2022-12-07
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Abstract
The present study adapted the General Attitudes toward Artificial Intelligence Scale (GAAIS) to Turkish and investigated the impact of personality traits, artificial intelligence anxiety, and demographics on attitudes toward artificial intelligence. The sample consisted of 259 female (74%) and 91 male (26%) individuals aged between 18 and 51 (Mean = 24.23). Measures taken were demographics, the Ten-Item Personality Inventory, the Artificial Intelligence Anxiety Scale, and the General Attitudes toward Artificial Intelligence Scale. The Turkish GAAIS had good validity and reliability. Hierarchical Multiple Linear Regression Analyses showed that positive attitudes toward artificial intelligence were significantly predicted by the level of computer use (β = 0.139, p = 0.013), level of knowledge about artificial intelligence (β = 0.119, p = 0.029), and AI learning anxiety (β = −0.172, p = 0.004). Negative attitudes toward artificial intelligence were significantly predicted by agreeableness (β = 0.120, p = 0.019), AI configuration anxiety (β = −0.379, p < 0.001), and AI learning anxiety (β = −0.211, p < 0.001). Personality traits, AI anxiety, and demographics play important roles in attitudes toward AI. Results are discussed in light of the previous research and theoretical explanations.
Citation
Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024). The roles of personality traits, AI anxiety, and demographic factors in attitudes towards artificial intelligence. International Journal of Human–Computer Interaction, 40(2), 497-514. https://doi.org/10.1080/10447318.2022.2151730
Publisher
Taylor & Francis
Journal
International Journal of Human-Computer Interaction
Research Unit
DOI
10.1080/10447318.2022.2151730
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Article
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ISSN
1044-7318
EISSN
1532-7590