Chatbot service quality in omnichannel retailing: Effects of ChatbotSQ on decision confidence and customer behavior

AI chatbots are increasingly being embedded in omnichannel retail ecosystems; however, research on how chatbot service quality relates to customer behavior is limited. In this paper, we conceptualize chatbot service quality ( ChatbotSQ ) as a formative construct comprising six dimensions: functional, integration, interface, safety, emotional, and personalization quality. Drawing on means-end chain theory, we examine decision confidence as a mechanism underlying the association between chatbot service quality and customers' share of time with the chatbot and omnichannel ecosystem lock-in and explore the moderating role of chatbot comfort level. We use partial least squares structural equation modeling, importance-performance map analysis, and necessary condition analysis to analyze survey data from the United States ( N = 345) and Saudi Arabia ( N = 302). The results show that chatbot service quality is positively associated with customers' share of time with the chatbot and omnichannel ecosystem lock-in in both countries. These relationships are mediated by decision confidence, which also emerged as a necessary condition for achieving both behavioral outcomes. Chatbot comfort level significantly moderates the relationship between ChatbotSQ and decision confidence in Saudi Arabia but not in the United States, indicating that this boundary condition is context-sensitive across the two samples examined. We also validate a condensed six-item ChatbotSQ measure that provides a parsimonious alternative for research and practice. These findings advance retailing research by showing that AI chatbot service quality is associated with behavioral outcomes through customers’ decision confidence.