Ai-based counterfactual reasoning for tourism research
Date
2023-07
Authors
Xia, Haiyang
Muskat, Birgit
Li, Gang
Prayag, Girish
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
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.
Description
Keywords
Counterfactual reasoning, Artificial intelligence, Tourism, Decision-making, Big data
Citation
Collections
Source
Annals of Tourism Research
Type
Journal article
Book Title
Entity type
Access Statement
Open Access
License Rights
Creative Commons Attribution-NonCommercial-NoDerivs License
DOI
10.1016/j.annals.2023.103617