Explainable AI-Driven Recommendation Systems and Consumer Buying Behaviour: The Mediating Role of Trust and Perceived Personalization in E-Commerce

The rapid diffusion of artificial intelligence (AI) in e-commerce has transformed consumer interactions with digital platforms through algorithmic recommendation systems. However, their complexity and opacity raise concerns about transparency and trust. This study examines how explainable AI-driven recommendation quality influences purchase intention, focusing on perceived personalization and consumer trust. Using a quantitative, cross-sectional design, data were collected from 99 active e-commerce users through a structured questionnaire and analysed using partial least squares structural equation modelling (PLS-SEM) and bootstrapping. The measurement model demonstrated satisfactory reliability, convergent validity, and discriminant validity. Results show that explainable AI-driven recommendation quality significantly influences perceived personalization (β = 0.434, p < 0.001) and consumer trust (β = 0.297, p = 0.002). Perceived personalization significantly enhances consumer trust (β = 0.371, p < 0.001), while consumer trust positively affects purchase intention (β = 0.588, p < 0.001). Significant indirect effects were also identified, including a serial indirect effect through perceived personalization and consumer trust (β = 0.095, p = 0.002). The findings highlight explainability as an important mechanism shaping consumer perceptions, trust, and purchase intention in e-commerce. Keywords: Explainable Artificial Intelligence (XAI), AI-Driven Recommendation Systems, Perceived Personalization, Consumer Trust, Purchase Intention, E-Commerce, PLS-SEM