What factors contribute to the acceptance of artificial intelligence? A systematic review

• Published in: Telematics and informatics Vol. 77; p. 101925
• Main Authors: Kelly, Sage, Kaye, Sherrie-Anne, Oviedo-Trespalacios, Oscar
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-02-2023
• Subjects: Human factors; Machine learning; Psychosocial models; Social robotics; User acceptance; AI; Psychosocial models; Social robotics; Human factors; Machine learning; User acceptance
• ISSN: 0736-5853; 1879-324X
• DOI: 10.1016/j.tele.2022.101925

•Reviewed 60 studies on user acceptance of artificial intelligence.•This is the first paper to provide a comprehensive overview of the psychosocial models and factors influencing user acceptance of AI.•In some cultural scenarios, it appears that the need for human contact cannot be replicated or replaced by AI.•Perceived usefulness, performance expectancy, attitudes, trust, and effort expectancy significantly and positively predicted intention, willingness, and use behaviour of AI across multiple industries.•Future studies should examine role of bias (e.g., job security, pre-existing knowledge) in users’ intentions to use AI.
Artificial Intelligence (AI) agents are predicted to infiltrate most industries within the next decade, creating a personal, industrial, and social shift towards the new technology. As a result, there has been a surge of interest and research towards user acceptance of AI technology in recent years. However, the existing research appears dispersed and lacks systematic synthesis, limiting our understanding of user acceptance of AI technologies. To address this gap in the literature, we conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and meta-Analysis guidelines using five databases: EBSCO host, Embase, Inspec (Engineering Village host), Scopus, and Web of Science. Papers were required to focus on both user acceptance and AI technology. Acceptance was defined as the behavioural intention or willingness to use, buy, or try a good or service. A total of 7912 articles were identified in the database search. Sixty articles were included in the review. Most studies (n = 31) did not define AI in their papers, and 38 studies did not define AI for their participants. The extended Technology Acceptance Model (TAM) was the most frequently used theory to assess user acceptance of AI technologies. Perceived usefulness, performance expectancy, attitudes, trust, and effort expectancy significantly and positively predicted behavioural intention, willingness, and use behaviour of AI across multiple industries. However, in some cultural scenarios, it appears that the need for human contact cannot be replicated or replaced by AI, no matter the perceived usefulness or perceived ease of use. Given that most of the methodological approaches present in the literature have relied on self-reported data, further research using naturalistic methods is needed to validate the theoretical model/s that best predict the adoption of AI technologies.