Trusting the Machine: How Consumer Trust in Artificial Intelligence Shapes Future Adoption Intentions

Amirhossein Pezeshgi; Mohsen Valiei Abarghoei; Masoud Naeimi; Qazale Family

doi:10.31181/msa31202640

Authors

Amirhossein Pezeshgi Price College of Business, University of Oklahoma, USA Author https://orcid.org/0009-0002-4241-8766
Mohsen Valiei Abarghoei Management and Business Department, Electronic Branch, Islamic Azad University, Iran Author https://orcid.org/0009-0001-1810-0144
Masoud Naeimi Management and Business Department, South Tehran Branch, Islamic Azad University, Iran Author https://orcid.org/0009-0009-6995-8050
Qazale Family Faculty of Business, University of Hamburg, Germany Author https://orcid.org/0009-0008-3871-8217

DOI:

https://doi.org/10.31181/msa31202640

Keywords:

Artificial Intelligence, Consumer Trust, Artificial Intelligence Adoption, Human- Artificial Intelligence Interaction, Artificial Intelligence Literacy

Abstract

Although the use of artificial intelligence (AI) by consumers is growing, one of the most important factors influencing whether or not they incorporate AI into their daily lives is trust. This study examines the relationship between trust and the intentions to adopt AI-driven products in the future as well as their current use. Based on the Technology Acceptance Model (TAM) and related frameworks, we argue that perceived knowledge about AI helps to explain how trust translates into behavioral intentions and that trust functions as a fundamental mechanism influencing openness toward AI technologies. We also investigate whether this relationship is moderated by education and age. Regression results from a survey of 205 consumers provide strong support for H1: future adoption of AI is significantly predicted by trust in AI. H2 is supported by mediation analyses, which show that the trust-adoption relationship is partially mediated by perceived knowledge. Although older and less educated people descriptively reported lower trust and adoption intentions, H3, which proposed moderation by age and education, was not statistically supported. Separate trust-based segments with various behavioral patterns are identified through cluster analysis. By highlighting trust and user knowledge as key factors in AI adoption contexts, these findings go beyond the Technology Acceptance Model/Unified Theory of Acceptance and Use of Technology. They also recommend that companies looking to increase AI acceptance should give top priority to improving transparency, lowering uncertainty, and informing customers about AI capabilities.

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