Does information overload attract or Repel self-driving tourists?

Abstract

In the digital age, tourists face an increasing amount of travel-related information, leading to more severe information overload, particularly evident in self-driving tour decision-making. However, current research often overlooks the impact mechanisms of this overload on individual psychological states and travel behaviors, especially in emerging tourism markets. This study aims to explore the impact mechanism and boundary conditions of information overload on the travel intentions of potential self-driving tourists. Based on the Stimulus-Organism-Response (SOR) theoretical framework, a moderated chain mediation model is constructed to deeply analyze the mediating roles of organism factors such as perceived risk, attitudes, and destination image, as well as the moderating effect of self-driving tour knowledge. The study used convenience sampling to collect data through an online survey from 728 potential tourists interested in visiting Shangri-La, China. The collected data were analyzed using structural equation modeling (SEM) and the bootstrap method to test the theoretical model. The results indicate that information overload significantly positively affects perceived risk and negatively impacts destination image, while its effect on attitudes is not significant. Meanwhile, the study finds that although information overload does not have a significant direct impact on travel intentions, it can indirectly influence tourists' travel intentions through the mediating effects of perceived risk and destination image, as well as the chain mediation effect. The results also validate the chain mediation effects of perceived risk and attitude between information overload and travel intentions. Additionally, the study reveals that extensive self-driving tourism knowledge can weaken the positive impact of information overload on perceived risk and reduce its negative impact on destination image, thereby enhancing potential tourists' travel intentions. Finally, the study uncovers the heterogeneous effects of socio-demographic characteristics, such as gender, age, income, and geographical location, on self-driving tourism behavioral decision-making. This study provides new insights into the self-driving tourism decision-making process and offers practical guidance for online tourism service providers and destination management organizations in optimizing information presentation, reducing perceived risks in self-driving tourism, and enhancing destination attractiveness.