Navigating Technological Shifts: An Examination of User Inertia and Technology Prestige in Large-Language-Model AI Chatbot Transition

Abstract

As large-language-model (LLM) AI chatbots create new pathways for problem-solving, they pose a challenge to the dominance of traditional search engines and knowledge sharing platforms. Utilizing theories of Status Quo Bias and Symbolic Consumption, this study investigates how user inertia and the perceived prestige of LLM AI chatbots influence the transition between platforms. A national survey comprising participants who have used LLM AI chatbots, knowledge sharing forums, and search engines (N = 800) within China was conducted. The findings obtained from structural equation modeling reveal that user inertia, rooted in habits and sunk costs, inhibits platform transition; however, the prestige of LLM AI chatbots can promote adoption and mitigate inertia; and entrenched media habits could, surprisingly, facilitate openness to new technologies. Implications on platform migration behavior amid technological advancements are discussed.