Abstract
Artificial Intelligence (AI) has transformed various industries, including the tourism and hospitality industry. AI Assistants are one example of AI adoption in the tourism and hospitality industry. AI assistants can aid travellers in searching for information and simplify their travel planning process. They can also help travel companies to deliver personalised, efficient, and effective customer service. Though research has hinted about how AI systems are transforming travel experiences, little research to-date has focused on how travellers evaluate the use of AI Assistants. Therefore, this research aimed to identify the determinants of user satisfaction of and behavioural intentions towards AI Assistants in the context of travel. Specifically, this thesis explored which factors determine user satisfaction of Human-AI Assistant interaction process and outcomes, and the willingness to adopt and use AI Assistants in the future for travel purposes.
Following a mixed-methods explanatory sequential design, this thesis consists of four distinct studies, including: Study 1: systematic literature review (n = 18), Study 2: a qualitative study using content analysis (interviews, n = 42), Study 3: laboratory experiments (n = 70), and Study 4: online surveys utilising Partial Least Squares Structural Equation Modelling (PLS-SEM) (n = 1066). The four studies are interrelated. Considering the context of the study is relatively new, this thesis applies abductive approach to provide better insights into the use of AI Assistants. The conceptual model was developed based on the findings from qualitative phases, the updated IS Success Model, and the extended Unified Theory of Acceptance and Use of Technology (UTAUT2). The findings indicated that perceived, perceived safety, and expectancy are core determinants of user satisfaction with information, while perceived safety and expectancy have significant influence on user satisfaction with interaction process. The results confirmed the important positive role of user satisfaction in user behavioural intentions.