Symmetric and Asymmetric Modeling to Understand Drivers and Consequences of Hotel Chatbot Engagement

Drawing on action identification and complexity theories, this study explores lifestyle congruency and chatbot identification as drivers of engagement, leading to chatbot advocacy. Data collected from 304 individuals were assessed symmetrically through PLS-SEM. Moreover, configuration causal paths were assessed through fsQCA. Findings reveal the role of chatbot identification in the relationship between lifestyle congruency and customer chatbot engagement. Lifestyle congruency and chatbot identification significantly influence all the dimensions of chatbot engagement. Nevertheless, only customer referrals and customer influence lead to chatbot advocacy. Findings from fsQCA reveal six and seven different paths, leading to high and low levels of chatbot advocacy, respectively. This is one of the first studies to apply both symmetrical and asymmetrical analysis to examine different casual paths to chatbot advocacy.