Ai-based counterfactual reasoning for tourism research

Abstract

This research introduces a novel method for uncovering potential causal relationships in tourism literature through artificial intelligence (AI)-based counterfactual reasoning and big data. Tourism generates massive volumes of device, transaction, and user-generated data, and these can be leveraged using AI algorithms to better understand tourism-related social phenomena (Park, Xu, Jiang, Chen, & Huang, 2020). Existing tourism studies have used deductive, fuzzy, inductive, and transductive AI models (Cevikalp & Franc, 2017) to extract insights from big data, but these often fail to capture potential causal effects (Guidotti, 2022), which is problematic for two reasons. First, decision-making by tourism stakeholders cannot be improved if AI models mainly rely on spurious correlations (Law & Li, 2007). Second, the failure of capturing potential causal effects in big data diminishes its perceived value for both tourism scholars and practitioners.