What shapes chatbot service quality? Insights from a systematic literature review in AI-driven customer interactions

Abstract

The widespread adoption of artificial intelligence chatbots has established chatbot service quality as a significant area of research in service and marketing literature. In this context, the present review aims to systematically analyze prior research on chatbot service quality and offers the comprehensive synthesis to advance existing knowledge, identify research gaps and guide future research directions in this emerging research area. The chatbot service quality literature has been collected from journals indexed in Scopus. This study later analyzed 49 selected articles with the scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR) protocol and the theory, context, characteristics and methodologies (TCCM) framework proposed by Paul et al. (2021) and Paul and Rosado-Serrano (2019). Future research directions are presented following the TCCM framework. The previous literature on chatbot service quality primarily centers on technology adoption and service quality theories, particularly in online banking and e-commerce sectors. Key dimensions identified include information quality, system quality and service quality, often examined integrated with adoption and post-adoption constructs. Methodologically, quantitative research, including survey-based designs and structural equation modeling, dominates the prior literature. This study provides a unified synthesis of the chatbot service quality research by integrating fragmented literature and clarifying conceptual understanding of the construct. This study uses the novel SPAR-4-SLR protocol to structure the review and the TCCM framework to shed light on the chatbot service quality from the perspectives of theories, contexts, characteristics and methodologies. The future research scope further identified by using the abovementioned framework and methodology provides insights for researchers and practitioners.