Technology acceptance theories and factors influencing artificial Intelligence-based intelligent products. (April 2020)

• Record Type: Journal Article
• Authors: Sohn, Kwonsang; Kwon, Ohbyung
• Is Part Of: Telematics and informatics. Volume 47(2020)
• Publication Date: 2020-04
• Subjects: AI-based intelligent products -- Technology adoption -- Purchase intention -- Technology acceptance theory -- Decomposition analysis; Telecommunication -- Periodicals; Computer networks -- Periodicals; Télécommunications -- Périodiques; Réseaux d'ordinateurs -- Périodiques; 384
• DOI: 10.1016/j.tele.2019.101324
• Languages: English
• ISSNs: 0736-5853

Abstract

Highlights: This study compared technology acceptance theories in terms of AI-based intelligent products. VAM performed best in modeling for understanding the acceptance of AI-based intelligent products. Enjoyment is a crucial factor in terms of AI-based intelligent products, unlike other products. First applied decomposition analysis in IS research for quantifying the influence among factors. Users considered the characteristics of combined products rather than the AI technology itself. 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.