| Abstract [eng] |
The research investigates the factors influencing consumers’ intention to use generative artificial intelligence for purchasing high-involvement goods. An anonymous survey of 318 Lithuanian respondents, recruited through convenience sampling, was conducted, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the SmartPLS4 software. The findings reveal that social influence, innovativeness, and anthropomorphism significantly shape both performance expectancy and effort expectancy. Consequently, these expectations affect consumers’ emotional and cognitive responses, ultimately driving their intent to adopt—or reject—generative AI-based solutions. Notably, introducing cognitive attitude as an additional variable, beyond the traditionally emotion-focused AIDUA framework, substantially enhanced the explanatory power of the model, underscoring the importance of both emotional and rational assessments in AI acceptance. Directions for future research include exploring additional factors—such as trust and hedonic motivations—and broadening the cultural and demographic scope to enhance our understanding of how generative AI will continue shaping consumer behavior. Practical implications emphasize the importance for AI developers to embed human-like attributes and ease of use into their solutions, while marketers of high-involvement products should adapt their strategies to accommodate AI-based recommendations, such as those provided by ChatGPT. However, the convenience sampling design and cross-sectional approach may limit the generalization of the results, while certain measurement constructs approached critical reliability thresholds, suggesting a need for future refinement. |
