The Human Element in the Machine Age: A Multi-Factorial Analysis of Artificial Intelligence Adoption
Year: 2026
Type: article
Study design: Observational Study
Abstract: Artificial intelligence (AI) has become a transformative force, reshaping industries and daily life. However, its successful integration is not guaranteed and depends heavily on user acceptance. This study presents a comprehensive analysis of the psychological, social, and emotional factors that determine an individual's willingness to adopt AI technologies. While existing research often examines these factors in isolation, this paper proposes and tests an integrated model that synthesizes key constructs from established technology acceptance theories with critical emotional and contextual variables. We designed a cross-sectional survey-based study involving 580 university students from diverse academic backgrounds. A structural equation model (SEM) was developed to analyze the complex interplay between performance expectancy, effort expectancy, social influence, hedonic motivation, perceived risk, AI anxiety, and trust, as they collectively influence the behavioral intention to use AI. The results reveal that performance expectancy and hedonic motivation are the most significant direct predictors of adoption intention. Social influence and trust act as crucial mediating variables, channeling the effects of other predictors. Notably, AI anxiety emerged as a powerful negative predictor, capable of overriding a user's perception of the technology's utility. The model demonstrated a strong fit, explaining a substantial portion of the variance in AI adoption intention. These findings underscore that user acceptance is a multifaceted phenomenon, driven by a combination of utilitarian calculations, emotional responses, and social pressures. This research provides a nuanced framework for developers, educators, and policymakers, offering actionable insights for designing human-centered AI systems that foster trust and mitigate user apprehension, thereby facilitating more effective and ethical technological integration.
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7219927598