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    Does compliance with the preventive health behaviours against COVID-19 mitigate the effects of depression, anxiety and stress? (Cumplir con los comportamientos preventivos de salud contra el COVID-19 reduce los efectos de la depresion, la ansiedad y el estres?)
    (Sage Publications Inc, 2021) Aydin, Fatih; Kaya, Feridun
    In the present study, the aim was to investigate the associations between compliance with preventive health behaviours, depression, anxiety and stress. 478 adults living in Turkey voluntarily filled out the online survey, which comprised demographic questions, Measure of Compliance with Preventive Health Behaviours against COVID-19, and Depression Anxiety Stress Scales, between 24-31 March 2020: during the initial phase of the COVID-19 pandemic in Turkey. Results revealed that compliance with preventive health behaviours has negative associations with depression, anxiety and stress. Adults' compliance with the recommended preventive health behaviours accounted for 14% variance in depression, 9% variance in anxiety and 10% variance in stress. The evidence might indicate that compliance with preventive health behaviours may protect people psychologically against the burden created by the novel COVID-19 pandemic.
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    The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence
    (Taylor & Francis Inc, 2024) Kaya, Feridun; Aydin, Fatih; Schepman, Astrid; Rodway, Paul; Yetisensoy, Okan; Kaya, Meva Demir
    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 (beta = 0.139, p = 0.013), level of knowledge about artificial intelligence (beta = 0.119, p = 0.029), and AI learning anxiety (beta = -0.172, p = 0.004). Negative attitudes toward artificial intelligence were significantly predicted by agreeableness (beta = 0.120, p = 0.019), AI configuration anxiety (beta = -0.379, p < 0.001), and AI learning anxiety (beta = -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.