Examining the impact of AI chatbot quality on user proactive engagement, considering the roles of trust, cognitive absorption, motivation and decision making: A study of users’ perceptions in the context of the hospitality sector in the UK

Abstract

As artificial intelligence continues to reshape service experiences, chatbots are no longer viewed as simple support tools, but as frontline representatives of brand experience. While many organisations have adopted AI chatbots to streamline operations, far less is understood about how users psychologically engage with these systems, particularly in high-contact, emotionally sensitive industries like hospitality. This study addresses that gap by developing and validating a comprehensive framework to explain how the perceived quality of AI chatbot systems influences user proactive engagement. The research is situated in the UK hospitality sector, where the pressure for seamless, reliable, and emotionally responsive service is especially high. The conceptual model integrates two complementary theoretical foundations: Expectancy Violation Theory, which explains how people respond to unexpected experiences in human–machine interactions; and the System and User Characteristics for Cognitive Absorption framework, which explores how technological and psychological factors influence deep engagement with digital systems. Together, these theories guide the model’s development and interpretation. The study conceptualises AI chatbot quality as a multidimensional construct, encompassing usefulness, ease of use, system quality, and service quality, and explores its influence through five psychological mediators: trust, cognitive absorption, motivation, decision comfort, and decision confidence. A sequential mixed-methods approach was adopted. In the qualitative phase, 14 interviews with hospitality professionals and academic experts helped refine the constructs and ensure contextual fit. The quantitative phase was based on responses from 413 participants who had experience interacting with chatbots in hospitality settings. Partial Least Squares Structural Equation Modelling was used to test the model. The findings offer both theoretical and practical insight. Trust, shaped by perceptions of competence, benevolence, and integrity, played a central role in translating system quality into user confidence and motivation (Rezaei et al., 2024). Cognitive absorption emerged as essential for sustained engagement, reinforcing the need for chatbots to offer not just functional value but immersive, enjoyable experiences (Sarraf et al., 2024). Motivation proved to be multidimensional, with users driven by hedonic, functional, cognitive, and social incentives (Kautish et al., 2023). Importantly, the study distinguishes between decision confidence and decision comfort. While both relate to user assurance in decision-making, only decision comfort—how emotionally at ease users feel, significantly predicted engagement. This points to the growing importance of emotional intelligence in AI design (Barta et al., 2023; Castelo et al., 2019). Beyond its empirical contributions, the study offers a practical framework for hospitality organisations aiming to improve their AI service strategies. The results suggest that successful chatbot deployment requires more than technical functionality, it also demands psychological sensitivity. Chatbots that communicate clearly, build trust, and ease decision-making anxiety are more likely to encourage repeat engagement and foster stronger user relationships. In short, this research reframes AI chatbots not just as automated agents, but as emotionally intelligent touchpoints. By aligning technical performance with psychological experience, organisations can create more human-centred AI interactions that resonate with users and deliver lasting value.