AI Chatbots and Customer Experience: The Role of Service Quality Dimensions, Trust, and Generational⁠ Differences

Abstract

Artificial intelligence (AI) chatbots are increasingly adopted as frontline service agents; however, limited empirical evidence explains how multiple chatbot service quality dimensions jointly influence customer outcomes. This study examines the effects of empathy, personalization, accuracy, and response speed on customer satisfaction, trust, and loyalty. Drawing on the Stimulus–Organism–Response (S-O-R) framework and relationship marketing theory, trust is examined as a mediating variable, while generational differences are explored as a moderating factor. Data were collected from 100 chatbot users across e-commerce, banking, and food delivery sectors using a structured online survey. Structural equation modeling (SEM) results indicate that empathy (β = .51, p < .001), personalization (β = .48, p < .001), accuracy (β = .55, p < .001), and response speed (β = .42, p < .001) significantly predict customer satisfaction (R² = .71). Customer satisfaction significantly influences trust (β = .68, p < .001), which in turn predicts customer loyalty (β = .72, p < .001). Mediation analysis confirms that trust partially mediates the satisfaction–loyalty relationship (indirect effect = .49, p < .001). Multi-group SEM provides exploratory evidence that the effects of empathy (Δχ² = 8.47, p < .05) and personalization (Δχ² = 7.89, p < .05) on satisfaction are stronger among younger users (Generation Z and Millennials) than older users (Generation X and Baby Boomers). The findings highlight the integrated impact of chatbot service quality dimensions and the critical role of trust in fostering customer loyalty.