This study investigates how inaccurate information provided by ChatGPT impacts travelers’ acceptance of recommendations. Six experiments were conducted based on the accessibility-diagnosticity framework. These examined the moderating role of the prominence and type of incorrect information and their effects on decision-making. The results show that participants perceived more accuracy and trustworthiness, leading to stronger intentions to visit when incorrect information was absent. However, there was a decline in their intentions to visit when incorrect information was present and more prominent or in the same domain. This effect diminished when multiple domains were involved or when participants were focused on the initial task. The research highlights that both the prominence and type of incorrect information are boundary conditions and provides insights into AI applications in tourism. Furthermore, it offers practical implications for online travel agencies in terms of user interface and user experience design planning.

This study proposes and verifies the slow city tourism evaluation index (SCTEI). SCTEI offers scholars and practitioners the capacity to assess the attractiveness and/or performance of slow cities in the context of sustainable tourism. The study participants consisted of a panel of slow city and sustainable development experts. These professionals were engaged in a Delphi-analytic hierarchy process for the purposes of scale development and verification. Four rounds of surveying were conducted and generated a multi-dimensional structure, consisting of seven domains, 18 subdomains, and 60 items. Locality, conviviality, and education were outstanding amongst the seven domains. The newly developed SCTEI provides practical guidelines for ascertaining a slow city’s sustainable tourism performance and offers a standardized instrument for comparison and benchmarking purposes.