Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products

By Kwonsang Sohn, O. Kwon. Telematics and informatics, 2020

534 citations recorded by the source corpus.

Abstract The rapid growth of artificial intelligence (AI) technology has prompted the development of AI-based intelligent products. Accordingly, various technology acceptance theories have been used to explain acceptance of these products. This comparative study determines which models best explain consumer acceptance of AI-based intelligent products and which factors have the greatest impact in terms of purchase intention. We assessed the utility of the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Value-based Adoption Model (VAM) using data collected from a survey sample of 378 respondents, modeling user acceptance in terms of behavioral intention to use AI-based intelligent products. In addition, we employed decomposition analysis to compare each factor included in these models in terms of influence on purchase intention. We found that the VAM performed best in modeling user acceptance. Among the various factors, enjoyment was found to influence user purchase intention the most, followed by subjective norms. The findings of this study confirm that acceptance of highly innovative products with minimal practical value, such as AI-based intelligent products, is more influenced by interest in technology than in utilitarian aspects.

View Paper: https://www.semanticscholar.org/paper/d284a24667edbae39b9c09e41a892fd14838c07e

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Citations in the corpus

• Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives (2020, methodology): “A study demonstrates that hedonic aspects, such as enjoyment and curiosity about AI technology, are stronger in predicting behavioral intention to use AI products than utilitarian aspects (e.g., usefulness) [14].”
• Trustworthy Artificial Intelligence: A Review (2022, background): “Sohn and Kwon [184] compared various technology acceptance models that can be implemented in AI to increase users’ acceptance and satisfaction.”