Abstract:
This work seeks to explore how the switching intentions of traditional human librarian services to AI-based chatbots depend on factors among students. Research is directed bythe Push-Pull- Mooring (PPM) theory, and its aims were to find out the key influencing factors, discuss the validity of the PPM theory, and to examine the relationships between variables. The study design was a cross-sectional survey that included 181 university learners. A hybrid data analysis approach that incorporates the Partial Least Squares Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN) was used. The results confirm that the push factors (dissatisfaction with the existing services) and pull factors (perceived responsiveness of AI chatbots) have a strong and positive impact on switching intentions. AI resistance was also determined to be augmented by the mooring effects of sunk cost and loss aversion. The research had two new and conflicting observations: the positive past AI experience was discovered to elevate AI resistance, and AI resistance was discovered to enhance switching intention. Its model showed a good explanatory power with 59.4 percent and 54.3 percent of variance in AI resistance and switching intention respectively. These findings have provided a delicate insight into the behaviour of the users and have given significant practical implicationsto the library administrators.
