Intuition and Artificial Intelligence (AI) : how the perception of AI influences the acceptance of AI advice

• Autor(a) principal: Hesse, Olaf
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.14/43246
• topic: Artificial intelligence; AI advice; Intuition; Rationality; Strategic decision making; Acceptance of advice; Closeness; Trust; Human-computer interaction; Inteligência artificial; Conselhos de IA; Intuição; Racionalidade; Tomada de decisões estratégicas; Aceitação de conselhos; Proximidade; Confiança; Interacção homem-computador; Domínio/Área Científica::Ciências Sociais::Economia e Gestão

With the rapid pace of technological advancements, Artificial Intelligence (AI) is becoming increasingly important in a variety of domains, especially in strategic decision making. The advancements of AI's human-like traits and intuitive abilities have transformed it into a powerful decision tool. This thesis seeks to find out how individuals perceive AI, and how these perceptions influence their acceptance of advice provided by AI. Therefore, two studies were conducted to test seven hypotheses, which were rooted in the existing literature on human-computer interaction. The findings suggest a complex relationship between these variables. While both intuitive and rational perceptions of AI contributed to building trust, fostering a sense of closeness did not necessarily translate into advice acceptance. A significant discrepancy between the results of the two studies was found for the influence of perceived intuition on advice acceptance, suggesting the role of additional variables such as personal attitudes or previous experience with technology. The results present valuable implications for managers aiming to increase acceptance of AI systems, emphasizing a more comprehensive approach that builds trust and enhances both intuitive and rational aspects. Despite certain limitations related to sample diversity and unsuccessful experimental manipulation, the study paves the way for future research in this domain. It emphasizes on the need for AI systems to be not only technologically sophisticated but also intuitively human-like and trusted by users to drive advice acceptance.