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

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

Kelly, Sage (Queensland University of Technology)
Kaye, Sherrie Anne (Queensland University of Technology)
Oviedo-Trespalacios, O. (TU Delft Safety and Security Science)

2023

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.

AI
Human factors
Machine learning
Psychosocial models
Social robotics
User acceptance

http://resolver.tudelft.nl/uuid:89f83324-0a56-4598-9de3-862105b5c272

https://doi.org/10.1016/j.tele.2022.101925

0736-5853

Telematics and Informatics, 77

Institutional Repository

journal article

© 2023 Sage Kelly, Sherrie Anne Kaye, O. Oviedo-Trespalacios