Navigating the digital horizon: how AI-powered chatbots are redefining engagement in online travel agencies (OTAs)

Purpose In the rapidly evolving landscape of online travel agencies (OTAs), the integration of AI-powered chatbots represents a transformative innovation that has fundamentally changed how travelers interact with services. These intelligent virtual assistants provide real-time support, personalized recommendations and instant solutions, effectively addressing the diverse needs of users. This study aims to investigate the factors that influence both the adoption and continued use of chatbots by OTA customers. To achieve this, authors propose an integrated conceptual framework that combines the Technology Acceptance Model (TAM), the Expectation–Confirmation Model (ECM) and the Information Systems Success (ISS) model, providing a comprehensive understanding of user acceptance, satisfaction and system effectiveness in the context of chatbots. Design/methodology/approach This study uses a multi-method approach (qualitative and quantitative) to examine the interaction between individuals and chatbots in OTAs. In-depth, semi-structured interviews were used to gather data, which was then analyzed using a grounded theory methodology. Subsequently, quantitative hypotheses are formulated based on the findings of the qualitative inquiry. The study sample comprised 445 customers who were familiar with and had prior experience using travel AI chatbots. The research model was tested using Partial Least Squares structural equation modeling and artificial neural networks (ANNs). Findings The study’s findings reveal that all examined factors significantly influence user satisfaction, which in turn drives the continued usage of chatbots; however, the moderating effects of self-representation and intimacy were found to be insignificant. Originality/value This study makes a methodological contribution by using a rigorous mixed-method approach that combines qualitative interviews, a quantitative survey and advanced ANN analysis. This comprehensive design enables a nuanced understanding of the factors influencing customer satisfaction and the continued use of AI-powered chatbots in OTAs. Theoretically, the study advances knowledge by integrating the TAM, ECM and ISS model into a unified framework. The findings contribute to these models by demonstrating how their constructs interact and adapt specifically within the domain of chatbot-enabled services in emerging tourism markets, such as India, thereby offering both conceptual synthesis and domain-specific insights.