Diffusion of Innovation Factors Influencing User Trust and Adoption of AI-Based Cybersecurity Tools

The adoption of artificial intelligence (AI)–based cybersecurity tools introduces challenges, positioning user trust as a critical determinant of acceptance. While Diffusion of Innovation (DOI) theory explains adoption, limited empirical work integrates DOI with trust mechanisms. This study examines the mediating role of user trust between DOI attributes and intention to use AI-based cybersecurity tools. Data were collected from 420 participants and analyzed using partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) analysis. Results indicate that Complexity (β = 0.323, p < 0.001) and Observability (β = 0.231, p = 0.002) significantly influence User Trust, whereas Relative Advantage, Compatibility, and Trialability are not significant predictors. User Trust predicts Intention to Use (β = 0.496, p < 0.001) and mediates the effects of Complexity and Observability. ANN results corroborate the dominant predictive roles of Complexity and Trust. Findings support a trust-embedded diffusion framework for AI cybersecurity adoption and identify limitations of classical DOI theory in autonomous, high-risk environments.