Switching Intentions of Bangladeshi University Students Between Different AI Tools: A Push–Pull–Mooring Perspective

Abstract

The purpose of this study is to investigate Bangladeshi university students' switching intents between different AI tools using the Push-Pull-Mooring (PPM) paradigm to discover psychological, social, and contextual aspects that influence their decisions. The study aims to investigate how push factors (e.g., poor trust), pull factors (e.g., ease of use, social impact), and anchoring factors (e.g., switching costs) combine to influence switching behavior. 350 students participated in a self-administered survey using validated measurement scales as part of a quantitative research approach, and 320 valid responses were obtained. To verify the validity of the measurements and test the suggested correlations, we performed Partial Least Squares Structural Equation Modeling (PLS-SEM) on the data using SmartPLS. The findings show that the most important factors influencing switching intention are perceived value, trust, social influence, and switching benefits; ease of use has a significant but smaller impact. Significant perceived benefits can overcome switching costs, which act as a barrier. The results show that in this setting, students' decisions to switch are a dynamic mix of how useful the technology is, how much their peers influence them, and how sure they are of their choice. This is in line with the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) of the PPM framework. This research enhances theoretical frameworks by integrating PPM and technology adoption constructs, while also offering pragmatic guidance for AI developers and educators on enhancing trust, usability, and perceived value to facilitate sustained adoption and prevent unnecessary transitions.