Modeling AI-Chatbot Service Quality and Purchase Intention: Mediating Mechanisms and the Moderating Role of Intrusiveness

Abstract

The rapid integration of AI-powered chatbots in e-commerce has reshaped how digital service quality influences consumer behavior. However, limited studies have examined how chatbot service quality impacts purchase intention through internal psychological mechanisms, particularly under the influence of perceived intrusiveness. This study investigates how AI-chatbot service quality affects consumer purchase intention, mediated by user trust, consumer experience, consumer engagement, and perceived privacy risk, and moderated by perceived intrusiveness. Employing the Stimulus–Organism–Response (S–O–R) framework, this research applies a quantitative explanatory method using a survey of 387 Zalora Indonesia users who have interacted with the platform’s AI chatbot. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4. The results show that chatbot service quality significantly enhances user trust, experience, and engagement, while reducing perceived privacy risk. These organism-level variables significantly influence purchase intention: trust, experience, and engagement positively, while privacy risk negatively. Moreover, perceived intrusiveness significantly strengthens the relationship between service quality and consumer experience. The findings offer new insights into the psychological pathways of AI-based service interaction and provide theoretical contributions to the S–O–R framework. Practically, the study guides e-commerce platforms in developing AI-chatbot systems that are not only efficient but also psychologically acceptable to users.

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