The Effects of Corporate AI Environment Factors on Employees’ Technology Acceptance Attitude and Continuous Intention to Use

As artificial intelligence (AI) becomes an imperative survival strategy for modern enterprises, numerous organizations continue to face implementation failures stemming from employees’ psychological resistance. To address the scarcity of empirical research regarding the practical and psychological acceptance of AI in workflows, this study investigates the structural relationships between technological maturity and users’ cognitive adoption. Grounded in an integrated framework utilizing the Technology Acceptance Model (TAM), this research conceptualizes the “AI Environment Factor” as a higher-order construct comprising four foundational sub-dimensions: AI System Quality, AI Information Quality, AI Data Quality, and AI Infrastructure. A quantitative survey was administered to 189 corporate practitioners who have utilized enterprise-wide AI systems for at least one year, and the empirical data were analyzed using Structural Equation Modeling (SEM). The analytical results indicate that the AI Environment Factor exerts a statistically significant positive effect on both Perceived Ease of Use and Perceived Usefulness. Furthermore, Perceived Ease of Use was found to positively influence Perceived Usefulness. Ultimately, both Perceived Ease of Use and Perceived Usefulness are significantly associated with the employees’ continuous AI Intention to Use. These findings theoretically contribute to the literature by providing a measurable structural model for the continuance intention phase of organizational AI adoption. Practically, this study offers actionable guidelines for corporate executives, emphasizing that securing rigorous data accuracy and a stable system infrastructure is indispensable for mitigating cognitive load, fostering innovative corporate cultures, and maximizing the efficacy of AI utilization.